{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:46:58.084756Z",
     "start_time": "2021-06-04T17:46:58.070756Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from toad.detector import detect\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "np.set_printoptions(suppress=True)\n",
    "pd.set_option('display.width', 180)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "attachments": {
    "%E6%95%B0%E6%8D%AE%E8%A7%A3%E9%87%8A.png": {
     "image/png": 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GGlnTQ+NClPgD07AYxmUS/pvjulyZ9tNjK76mZpZED1tiqG8mlEjDZJoDDrASV1yfRrc2NJqAFJrh/7MVAb980+ArLywXlhMJw6HwulLbRXuxT5vqk03owBRKAsnP6aQsuS0HMEtthb8CxZgL0I0GJMo5/KuYtqKpKji3xRlrybR9tO8OrH8ZZotfHm9xah6QPH8BbhUhSI0CKmwHJHiY+ZAdyXwEEn6yXOzR/FFwKxg5InE++Od/bhIMG7aJgkqRc8Q0yXtu6CsE96sPyNC08S1LvQLw1rU9Gk53lxMvwHmflKZbI6QKS/ANd9PTjmKldua/7NndbLRuFHyx5oRiDfzF4Xy5R5Smh0C/I6ZqEPBo1SrYTGOI/imiUT49T2i29DqBbxp879Q+dmpPKxahiBgXgg6O1j8QcSUutQXhkw0baclbibiHWDB0xNVr6iVA7hvOkVph0b+wfYU406F8K6StQwEL3VyMCT7CdN7C/3KSLXTfGG7+G3LCuEHZDgiKlAGBmqGg42HQ9n/Iz+lmvXLoj0LGtmUVs+IidfORge4Fk7ZHVfiNK0X/QMTxgX6eWhKSutnpAFRBmO/zexhwVMG+8Xp/iUTFjYekIogxFDc9VEpMU8oAVySRPBymLTxWzHG73CM+3hCo3Kf8fDnXWP5TNPBTcWSYRATcIDBxb5q4FAf2FVZFEavfXjfWCVQNJcFoVWOF8O4o+T44E8TRuLRv1U3j8zxk+jmaGP0ZYWvPFP0hLoLS4KFzsGvqpawQ/EUdyN9vkcrG8CuOKugA4lHyUXDYjiHvVK/ByEAczKmMHJCIan0pnGgMAmqa0LBz0r7UMHheIRXn70GXw5+A/go4DovL+QEKRyL53hUfu1xJq6yjpEezgFOun9wE6vLx4yu6TyWzw4ChMOZiYSnUT/UzDHwzAAmLPCIwYW+auEE0PavDeHGaJ1G+D4M5Tm7XBJFes3GY1sfOqHro0Rp46RAI2Hceyn50XAkKY+FmguZjSA70LiThNMDKaBWZlh+z8oUwcvNH7PEQSGmv37fEZx2w70wQHLA2oR8xA62vvRkf8u4ms0Q2DGHSzixvcBLIJ5750IsxqaeAA64VN8DlSseV/LtxtcbGnhGAUaeLHjkFqxo++vs1jQuhBXVaejw61zNnpEAEeAS8f9PYjhm8d5F903lentIB+7yK+vTEBuvVCIA90CrsLnXcuuZ4sANq4QynRGEVwMJT55dsZB8Q6uB1c5Y8tFp0Bw7B0yyJVnflRQ4GEPScNPCuc7tWQ0jXmSBYu8g+YvrTuL1gqyOhdz1l30PxpuiJbcc/EhCGGPWJOUFcd2fmBeBjp49Y+fcv7XTn/PFcaJer0VMGxeryseTkGSNuhgkMl9uPsZOD5UASrnYsvWCbmYnANL5pbOETH4DJ5mj/vGVcQP5FOmNtXFzmbGN5upSLGqMGK/X2MUtA3EMXPiD0RO27bEH/MI2rFSxmzevEdouD4C8OT/mDw5WZJqVvl25al7aQg9LDmUaHRQpwyZSCM4aVGf+nmy9nZJPosJR28pCmqGTQaIhAMN2UUDyYwA8+xRI43GtgjKK+FHW5GhNpIYHkMPdirwMVAwgxhRMkXex0eqgwejmELoQ3Xv4DUDHBzBxAYNreNLZ+SY4llr7LwukODqMTgKRP4Rx4KNNwi5LW1BzNwOlm7WedTmTQ5U9wVLyyxb3wOkGrnxO2d/pjuoPp1N8igb9q6bmk6kQQ1PQ6ZJg/WWiiXt2gmWfRNZz6Ai5OTS0qFUUaMNEwORhPCIeUe5ZR2QXWM62wly2bMaHE4rFbujh2WKr+2GEYpfWy7Xthz/wezh3iuRfyyMOHD2WyNRsj5DQmEAFEABFABBABRMDPEWg94wQJcb7czx8TiocIIAKIACKACHhAQGWX/+0P1z2QYzUiMD4Eftm2Z3wMsDUiMPMQeOOnNTNPaP+QOPwHV/1DED+W4od0AwG0y/34CaFoiAAigAggAoiAFwigLvcCJCRBBBABRAARQAT8GAHU5X78cFA0RAARQAQQAUTACwRQl3sBEpIgAogAIoAIIAJ+jADqcj9+OCgaIoAIIAKIACLgBQKoy70ACUmmGoHRzgMNJWXD4+p2aODMgYGRcbHAxogAIoAIzAwEUJfPjOc0x6QMiDuSlHyzvST7av+Y7zw4dOML905lNzR1jJkFNkQEEAFEYGYggLp8ZjynWS3laGet3gQPiHsjJjh6QaD+zjuG+/WFhiXxYcnR5PKXeuaG1FiICPgRAv3lpS1+JA6K4u8I4H7s3jwhx+sheSe0hC/VDtrXQ2Gzfd7ms+pKqUpdijkTBEZvV7aXVBpXnsruMqoIyapeYdFV9JddJdv58oC4F2JIcBBHCN77y+SNxLhgrowMN2W3X+YLaFrqouNqSeGgulKqUpdiDhGYGASabNaXPyDktZN5XvPrLX0xcV/H6qKmOpvF60ZIOHYEXB/9JK74gr59Yn5P4yvKXugc2S+qL5UL59DoW0FJ45FHfhWmakvJvn439Z92w2Hq7HLPAXW5CJPbn6T3Bq+9BxR9VUlJDS86Tu8M48jX2R84wvlyZ8WmmJDla+31jq2hHB0mjREYqXVdjo7ZciRUNMFhntv2TVSpRt1KbalmJVnVTxLwnMdLhcrvYE22Wu+mxiTfbCjpViho6jcDYXJ3NB+UUp2WAr+GXcev2F26gBdppLYNXPfBuUkbMwNoa7wQgYlFIKXU1RbzYqLhKNakp/DIKEL/JvCaIgR6+y7kHnv47vNcd6B3D5MyTpGDes7+HSjg/wEVTpX6qjxTdX4hL/t3JDGf4wbJC3kLK5Z3XnpoZcWMw0+OfvY/O55Rk4k5f9Hlzgp7XYpdpSMN5Z0JhRFbT3eRTTH2A8dS1Fp/Jgg/1TIODTRUjgTnxsq+9P5zXUMkkJwbCNsuaXeQCfzq8UEWMKA/XbClmpUbKHIme2rC7u28IQ6FoXHKXVETnLzAcVaqvE0FZiZuIW2nKi93JpgMOLzlhHSIwEQhkFZ4637hRDFT8+kvtzWnluaGq0vnds7ZE1anUuRgWH+4e/lWUe9ScJh6zj0m2uLWV357tDEy+0jW3QPpOujqd1WQ3GfJNVWF83jF+ySiTlDkUCNw2P9h/Q4DDlDvL/Plzp4e1X3M8EzE1iPvrOrcW+aY4fcx+eJDhFp1mmLgdlytaYROR4Yau8D2LZH/FbbX0Di4oBSVPT354pn0EJgZmxw90nIOZ+JNAMLiWYTAwA3cEV37OCN2vKJWyV+/+ytnyU7OTG/8f++TZ/mSiLTU58nv2PdNza3xSEH0W/ui1YWERESCq15Fz0q0ZHJ+vLrcWVF1XmbGEvoSdb1RrtmeedqofAaXhUbBszndpAFnBt/QVIg+3FQ4GHswLSsVJqTTdkv/sugkE5SEBQ5NhRDe9RGwaCmMxL/t944aqRCBmYpAk+3Vqpkq+9TJzYzyXbINTUh97e9AHUdxJcT6Dy8lkvdrNZPsF/J+FfZbQ7d56oGHd1VT7M4eJ0kMizS5Ky90OUwShyyfJ/6zK8oJYr5ClsfYexXO+hJWB/PHUvNNx/qIs9lBT1tl1/k3GWcaO9a5N0nsJaliQKoXfzkOy+e9KRu7A8eyaJPXmyE2jbXNqnLK0ipkjAmTTRSDr+LKX2+mlEpfwE3sfyw/EZHg2e250TeWtnOyDUSltZODaSnxxLL9SZe0Gg0mp2sahVizgEBVwNo0gxQYAtMC9+740fBimgHB7n1FoL98nXXJfPFffoNhc45mXSX3rVWIIepNYvJiuUspF1IQRifVWg274Jpbl9j4yPmWfCYbjcJrLUgU5TToQtvlHMzrjHLy9Q1wmBvq3WsDvFpxHv9/y/kpdjfgNR6J3B9RxwfWqYk9zJfTMC5757YPrznW0XY0m1XVVZMDxj8RY76490tfIjTp2f5gMIn1S7VvzCUI806iHAhZ//a1B2/DLyjjsmWamDJGAP+Bvs88DU1O06BxAhzS52XtZzKE7qypJ1npezc31UJsGg1MOxqzZ3/X4LUImm46/3YSa8LEtkcCjZClAwiRA7uLQbvAFg58harCyPoHg6HsxnsBdEFO2rOPV4QVxk+zauLARwB8I+8vg/DytBRRW0MwGmnKbnOlkss3nxInyL3k19heQr307q5Yd5Xe1gU+vQB0ubfUSIcIqBEATflq1WsnbzUn03JXZcZKa4Y2Cv3z/Pk3oq64blHjDpR6SqIt/FYpo+d4hds+uWWDPDAs5YrFJodWnLx1X2hCdbO6C8rzUOvLH9//JIW2pNmMUjkSPrn4vqsYihsOLjka0daM8+UUI8MLjHKS2sOb4ALZ8lC1+ngmajlRTYq7Pioim8v1DVW9yKHs/1xnNNcu086TUwaJvqpcpsjfY4pcJLjkk4YbqKvt3LZLUOTAALTvtQwIc+sj68MMOjQoou53fokXcCi/HpJX3JwjS7XtQ7Y2LCxsJYl78ddsnMEzoncBipzRsPL1b9e/k5XOcxDJz9tBkQvB5xC/9mArzwXTk4cAWOSO2y/IilzoKGAxTEiDVk61ymFx3kjgKbycxb55wwhpEIHJQ4A6rkGRy4rZmltX41ySdaLJVsjUKuv5A1Dkn+SJX3pLXsWhcytLy/ckSyUepOst3UcVudwFSS6+cihjpdIFJaCKnOuRkItdA4RYPLDGahUC4E5//p8+U6vtr6/BQjLQ3PqrrQ8sPEb89bvb+7IaX9GTqEue2dV4aRcUQRz7wlUF5nHs7nzszqaGL1btz+cUOdVwnFJUd2mYo9PGJ847+LqIrT7Eq58/f5ZsShHsaYlJUr497sS73jrA2V2Eq4E2kAqYnyApuIpMAnkKf4dGF4FFzselw9qwbMeN55PolPlz39IIOK92Yx29c9Oj2Gz5Gd+XxxZIgAhMNAItdR+QnP9PbWGnbStY83Fxab/S12s2ldq2hi8jHTd4B61Cqk/1N1Z3rI4JVVVQDh/Xic58RlC0TRk6EEtes4vT/aqmmDFDwPVRQeWzL6U9o65/ZnmiukDOSY535/HD1/7NOCJdplUlrK/8T/U/X9h/+F2XqljOzJNT+oSzp5NEb1drQT2VhxLwotfSSXFK5vuS64Eb3WRtJkQZqS7qvvbaPUDv4lJnTMhRFQvIbNIWbFsv+w+0VWPIO10w/MLLCwSCYbGZfLFtW2C5eXWaaI7D8u7qFf1lDdymMcL0udxEToze7g6MemOK1nyP/BEd7DLymPAJAVfvdRK/QftltUSscG8Wh0atIed6+kmaxYveBm60koutKUv2aWlzxAJKsGy/N6y0HDDPIeBsaLxAIg6J3hOuApJ0avx57jGzSXSR5EJR99byHWp6j7nUf/wF+d3Zhq93GcXKudPlHjl7SSBOikOU2eb0eXafNPrN65cIyfSyH3OyVcL8ujnBJNTQMcSq/RleTiVMggAzjCXdpwVWloOeZtu2qKW3bIft2S+T5xe0VA4SwjaE4beXEYiHvhsmC6zijLu6/STkRgZHSHRM2FR1Nwl3gCynCwGn8yIhGya/d9wGbvIx7un+iiSm6mLL2dR4pexOF+RgjvdcYRL9+fLMI48s3KkW8KvIhRCh4GZenJr773d/TYjGDUDZuNPlNBK7ts9JxDg1da++5yAybtDOtk5Lf916TZ7tdsto6bJV5IqOgpq8q9K48Y6Ogiugd9HN5aco6Th3Gkz/MC+FnCKh/LAbeYdUaosntGfDOnLQ1ibXUivY6CZ1ZKT9m6FUq8WseoLLh12NMJf/hE9z+RMsArKbqQhERKwmN3TC9zuvktXZaq+4isgnS5oa8RDNa375ZOWbs5njNbyprYIiPfOfSaXzhoukyya7awB02S8ypTXodMnZAbmN8/irkZ+l8nu40pL9pKTzJLfUzXwa3v1eMREpaWsvNUCcmvpyHNOtGVMI+nu/UDJ009PXNcRhOQc3kSsuCLAwv/qqjjULtaE7d730hb1SWQhHi2k83drMZC/VpBGhs/MAACAASURBVPFdKF2YizGOGmdF2QkS9872iXTaj0Mcv2062vnHJ8V15OImMIHJpcrKcnmJ+e7qJDglJfa5IPM7GW6vJMkb3BCYN/W9hu47SwKnrDvfBcQW/oyANTf/tY5Dv25RyehqPtcavyHdohRe7eUWCUE8+edV5OWMNKXebcqSmh1/sbpZxYGQ3tLKJrGZMQFpqNQvbOM74jjwxXM1zZSr4c1Tf/hXu48pq8mZN/6f2W4Zhg20hdTi11x0/xluNKCudRf7RqjehWXf3JpyunisKUreZpyGjp8tlLU1rAR7t2ctOZtJ13yzfsLCiD1dtV68r6rwdNyLKZrRJ9jfnZ80CQp+4Nie3qh1kpjr7LWbzmYqS73pmrS9ZH+lLINEaPobllNpJ3uT6NJ28QI595AMuQupeIJ+2bo7Gv+PG7h6RDQgLnNitC9VrqlW9YkpHnsfGwE9W/1U5UjsQdzAdWwAYitCUkpP5nzwaoYc6UbXpBWQoiJVsFtrgU0mgCVnWR+vVoWqecAx3FZUQAoSuVXpsJTcRtbJwW7hNlsOLBzn15TD8rPfh6tkgE7Ai9Ba1+hi3bkqbV3hMgcPEsyBamZqm9zn8+XVYJrvzAMHHlyuj/5l/1e/qHYb7MZC3GVu6TvznydfXZNHYxDHDnu2y5vCynRSYp6UMP6Fqe6uSHpSiFTNLw+DsqT3HPuTkugsOL1gWvrXabll2x+8rdijy+z1+eTAvJBORgH/xb1jsI48dOev938i8qEEfOA6lYGeViIGr0EA3QNRkTO9DhPqmzctcxwhe9hRZkn2qMGUc0lHwT2QGUKEpWg0/N5qn5e0fK8gBD99Tmfxz4qybV5+gqY09wglqnPSvhD5SGQSB6mc4TCoWxon9oE/k4AAbOp+4aktR8Y8LFCdkzZka2AGkxRhJ80CSOUEpsl9W/I+CXeMLGc6ArB6uwnWc0uxafEFV1xqJRpVcOWT1HrYB0Y8MSWnxlXnrVEugEPj0iNs1sT5BUJeN31OZYgCGeZLYPLL5KQyYs0tLapLXGk9BCVrDrUJC+Ll2rmdsIauhI3XzTAAL3pnGByp8ggjMDvlrH7XqoxKgcVXGQthtzhpvhwC1++G5i0UmwOFeEyLSXePPHz4UK762x/cTrDIdJhABMaKwC/b9pg3VelUPRls76pauiZSgKHct+jICou+AZYgAv6BwBs/rZlkQahl78xXLxaf5C6nhn34D3A3eE9I/zABKDzY5Z54YD0iMLEIwHy5oe8aFPZlYhA0zsrfSLRMrBTIDRHwcwTYgeVR0k4v/eVbwUXfhN5vP39qkyke2uWTiS7y1iHg1i7XUWMBIjArEJgEu5xNscPaNuECD3yxbx54qaW//6Jd7vkJoV3uGSOkQAQQAUTAHxGAbV/v5/qjYCjT9CDgNo59ekTCXhEBRAARQAQQAUTABwRQl/sAFpIiAogAIoAIIAJ+iADqcj98KCgSIoAIIAKIACLgAwKoy30AC0kRAUQAEUAEEAE/RAB1uR8+FBQJEUAEEAFEABHwAQHU5T6AhaSIACKACCACiIAfIoC63A8fyhwUiW5y3iRuVqm7/Y6rZ2pHdaVjLhjtrB0ec2NsiAggAoiAHyKAutwPH8ocFely4dV+k1sfqnS4V+cjtW0lZRoNDTvCNhi1Clg02F6SbdqXiQhYjAggAoiA/yLgl3u4Nlcds+TsDPNf1FCyyUAgODfMovAdbiojKdul41KiY9IyA5RKw1Rje4lwJJFSGxhLRkdIgP6UcXVfSgNMIQJ+gkB/eelAni3ZT6RBMfweAf+yy50Vm+aFLJ+3ucHvcUMBJxmBoe+GQTcfGBjxvp/UBO68czgEPSGWkMUJQXpF7j1LpEQEpgGBJpt1yfyUQ10+dA3bsy+Zb1UOUfWhKZL6MwJwitojcFSa8C/1I6e5rP6ly+nhpI79a83FxZpZjMDQoDIpPtL+zRAcLXokdII0MfW365zwsxhLvLWZjEBKqautKN6nOwiPjPKJHolnAAIX4MDTgujPHt69xP59VkKKI83VuX/pcgpvWBicCIvXHEQg+KarRJrhHhkcCX5+iZEiB63c1jk0BngCkzdIHvsxtMYmiIBfI5BWeOu+q85mmQQh+8ttlb2TwBdZukXAebzi/cT83+54RqJ6ZldZ/vNtxUXamUSx3i/nyyXZ8XcuIDBSe7UvgQVHPB+7+4W+kk9vjWQ+4WokQ8RRUqkAcCqbczna2oj+aFSj+XKcblQQxBQiMCYEBm5cJaljaomNxoFAT/dXpC2ih5AImYn1H15KLD7b8zVJlRW8XIfnlytQYGp6EBgZvEcSpK7jV+wG3+LQwDDvYO+4WvLpAs/+dpgv3x7QWXYrbLvgmQcL/ttFBkeeS33hLyKACHhEoMn2ahU59K8e6ZBgghGIjH6WkN/VNB5I924g5dnHfv7N5TQejf17vdnxOk1sOtbHyd1slwnmvelQKsRy+3lCxKA2fVtG7b6Wknjogor3ejMlVFhlVbkJE6CkePkDAh1Xa3QuI5gsD3rBt5nywMzE3ULQ+82uU2LEXFBK9QqLP9wjyoAIGCHQX74OYtzEf/nGAb8czTpjR7cQ9cb4vFju0vbDwujcdcE1ty6xtXDtW/KZbC9/QEhrQaIop0EXXBNMTiACETtOwjR5Oa/IXf99tu3Zl9IMjHLo14MuB0WeScofDF6j/xz7r2zOIx/Wv7Oq83q/KDPVnZtJrUAweK2W5M2Tleg6uxDIdu7N5bnkiMCky0727lFpWegixh4pcThC9uSdUOPhoQvaNYhE2wAroaMuexy51Iu6XA2kH+ZGOz8dJKnWOJX1PNx+4akE3yJ/VLcW6+M4QNUYM4jAlCAAmjLl0IqTMMlN/105dD1LH4X+ef78faSCEdxvKiAFiSpdK4oZbvuEMTmZo5WbjgNeJlIX908SbReUIHFf1MeCDNDF1Ve5SPjkYqG85mWy5lCbSPNJnlXbDeanCoH6Y8Xk6Fu7TJ6AW13eV1V4+qXat5NEUcNyKu1xJ87f3Flz7b11rKyvKpeqYft66WbWv13/DjlazExkqewsWX/NsTVUyEakpK291FAnm/W0i7h3HDKH0J015duklvTXqy5Yg/P2wsh6oSMaD89JxfPDtD8hEBB3JE20p2WxOr5b/IYbo3y404c94MYcKCdLgwlEYBIQoI7r107eKk0WeVtz62pevrjvRBPf1wc3oq7IutOSV3Fo9Qelesubb8Gne0v30bGC3AVJLr5yiHBdUILWlz++X5jCNbvYNcDlMOlHCDQeySDH/kcJhdOKNq7YN2dTwxer0pSZeco8NCqa7D3veG+dNAIgL20QFL/QtTpMXeBQGaYVS8571wUlP0FSHkgjBrk5JmYcAiPBS+KCYUtXR0u3SnY+9u0GSdoobx0Ds+mFgyrSwobLfP7ccNx2Po9pRGCaEWip+4Dk1EiKXBAmbVvBmpTi0m0pciz6azaVEWwNX0Y6boC30cQuU99Tf2N1x+ps0YQSqyiHgrqGwpQ0KGAERUWcIrfkNbvy1Fww5ycINB55pPYfH777vBtx3OrysJyDm5ZnvpnyQDTNHcX2zm0fykqaOHs6yaXOmJCj2g42aQvM8pRD9Hb1aEBF630X29YrgqlYYGZGIRAYTPd3A3s9ThZbF/s2MqSsRCcQLle9QqIFQ9y1WA5xp2qeZNF5dM32rhI5/iIC04CAq/c6id+g/exZIlYQZhZbTEQKjVpDzvX0kzQzAr7dwI1WcrE1Zck+vpCmJVc8JVi236Ktxrz/IQCK/FdhPY3uFDkI7VaXE7J++/61SXnzTou3t+1Dybsu3++q/V01Odq3Uq6dkMQUdDEhciKTqUJA0PcGvXV8ezn6qS387Hv0AqNF6gZNsQgRmCoEnM6LhGyY/N5WFzVNzorzyRcde5AQcH30E6rIX/GoZN3OlxPH60m9B6W4NgheE6fJpV4iIhXbSSrz7Zdy6O5zE6Q2/i58EwipZzIC/V8O8jvMjPzxHnc3I7fHssMMxwCTiMDEIBARsdqAUb/zKlkdo/aKq8iYJR1pUZWZZqgR7/aiBNfBysfLnxG4kBfXd8gLRQ734F6XL1226mymsCBNjk7nblwbyCZU9VUda+aI3CYjrJHagPPmJj6OffxduO0fK2cRAkMDbY2BUQnKESyweRxZ+gSzy2F9WlrKOGLjZxFMeCvTjoA1N/+1jkO/blEJ4mo+1xq/Id2iFF7tVe221vB5FXk5g051e3NZUrPjL1Y3qzgQ0ltaKYXXGROQhkr34XUcB2/EQJpxIPD1u6kVyzsPpKtYXMjbdUFVIGXc6/LQjMy4tfZyuuLr0tEYQanzi8tpZDvZm8QtN++rStpDMvhgN6kn49919tpNZzPlgQI0f7dnLencmySuFyfj78K4YyydbQj0n+siubHq5W0kOERR7bPthvF+Zi4CKaUncz7gFoC5KjNWFpCiIlWwW2uBrbRfuseW/KyPVxdt40LVpBqT33BbEV3Gxq1Kh6XkNrJO5hBus+XAwnF+nVvDwSW/D1fJAMzBi9Ba1+hi3bgqbV3hMgeTnrF4QhBwHj987d9OalagwcauV6KfMeTvbr4cFnbDcm26ymvrtZ1SayiMKXPslBaq0dVfVvu8pOV7BQJ+bhs2eNl8FoozQ86KE+1SCUlafl2ael//9rVaWGIuBNDR5tuL3yQOiT8096YL2vnm5cygf4lfI0fL8ZplCNCItnvJcoAbuzs4v7yGJOyWg9tp4TBsBBt0EHX5LHv8s+R2YPV2U/k6OTYtvuCKS61EowqufJJaD6efdQh3nFPjqvPWKBda0Lj0CJs1cX6BkNdNn1MZokCG+RKm/DI5qYxYc0uL6hJXWg9BCSw0b1aH38tkmJhYBL7+z8++er9t1fs6rr+oNtbljzx8+FAm/tsfrstpQgaOZaVf36WdI6dbsJ2XI9s5ckwiAl4g8Mu2PUZUdBHajedhpRnRr0YjsDnrhu/O2LrE+W5hr9YDfYuOrLAwXqDIG0istEqNX88WkqVs/QYh7u3yWrXYg+hyN3oOWDY5CLzx05rJYSxzpZa9M1+9WFyunMmJ8B9cncniT4nsP0yAbtzY5dTBvnfzpmWO0zvl9d/gA98MRrZ9SgTETuYWAkFPUxtatRpNASBoY7UqKijuiLgOrb+soS2EW25O6P4zcYRuxm5VFDkwYlPmhGn6pQk4d65Ai6mZiAA4zNmWbcJOL/3lW8FF34Te75n4KCdIZjd2OesBlHfS0S+UzmCPNk61K+WYQgS8QsDELveqLRIhAjMUgUmwy9kUO6xtEy7wwBf75oGXWvr7L9rlnp+QJ7uccQjLcQxKmwt4ZokUiAAigAggAlOAAGz7ej93CvrBLmYIAu7j2GfITaCYiAAigAggAojAHEYAdfkcfvh464gAIoAIIAKzAgHU5bPiMeJNIAKIACKACMxhBFCXz+GHj7eOCCACiAAiMCsQQF0+Kx4j3gQigAggAojAHEYAdfkcfvh464gAIoAIIAKzAgHU5bPiMc6SmxgdEbd209/PaGfthJ1B3t/hLSvYUa6kzFtivdC0+YGBEX2FhxLY6KahSdy60wOpx+qR2qudpqh6bI0EiAAiMDMQcLPv28y4AZRyFiEw2m5zyNus6u+rZTBh9/YgfblPJbBPXE0jofvCalkNN5WRFG0hIY3tJbDTu77cy167uxpql0j7y7I2QwOdQ6Fxbg5tg1PYgbDwqnrfOi/705Pda7E1tBB+O1s9DZYgAojAzEYAdfnMfn6zT3rjndKHBs7Y7iWaKFRRPXuHBfDfvZ2QDlCoQepD1YJSnrtaku3SnNpComO2mPTrVYfRMWmqE1/ISPs3LZVdN3L5fWdVnOAUdqOhhoqGwGa0ZbfCtoeyE101VQbZ2IPi3vUGdViECCACMx8Bj7qcnrCy95J4o+y4s4HzzWT9OtXm2BOAg3CE2qbyB9wJaRPAVmHheD0kTzwZnfay1OAuxi/D+DkoAs/BVMDiaHLb9/u2bGfqWd0Q/NunKhdwZ6uoq+ND49QFNBe/Yktu2ylbw23p5BV2ArrVS32p56cuEbaIf9JVOQLjg0R6GoLRRU9hh/L2Evq/h6vlJtlyxCt1PvzHURKPR8Z5wHMaqpts1pc/IJO5A2tL/vxXq4Q7o6ec5Yab3CVIUhzTVGezmNRj8bQgUL9rVUal1HNifk/jKxFSTvPrXpdT/Uc+vPZAOo/8/JvL520ma+3166USDbtxZtdGLh0nB+PmTMWC2A/g/Fa4IBtylsD5quty9LiMX4bxczC+i7lRermwwcTNHuIDAB1XT1WS2NR7NdltWjvbLZfATGtsZfsYNR91HkiHuSm9jJzK7pJzl7MHCbi7j4Ra5CJVYrTzN11BB9M2qjzw9DyY2y8YHew2NNw/RAKDVSwMM0ODo4SgLjfEZvoL46P036EJkQrOI8+6Aaep3rICu344YjXRFn6rNNmAd8NBGFKsLjKowaJpQ+BC3sKdV45+9vCucMjp1++m/lNkKjFT5+50+fk3867Y6x2c2oazxrsiN8X03CRkou3ydfYHg/bJAc3x+uazoMjpQezCRftKeT2kyQmHo/NdepLBWWGvS7Erp8bxbRXOdn0xlniPgBsfu7dMpAPOw9rbLqda44LpaadEMrU9MQlKOZjQL5qwo3dukuDnvVaBwaGaw9yob+DCU4rpLApm6u4eqb3csjRht0qRu5U3OMjith4qqWshOiSWfDdCgibIweCpS6z3HoGUUtetUu/J9ZT95bbm1FJDa7slnyryT6Rj0QdutELz3l6SrDPNW/KPkpw1hD/yWt8TlkwtAs7jFe+DIb5DPq38mV1l+WfjiosaXylPNRDl7wzKxKKBG91kpVXSfxJdxNbt26T0jPh1VpSdIC8dlBW5KHRSvp3c6PPtDpw9Pb41QOqpR0BS5NxcOD3t1PplQ0l2g1dR5fGyghy93U2Ek1i19wEmOHDzKcQdmhQOBufGcoKpuQ4NNIDiH8/cvJofy9HhCFn6pJXc8z2c3oAdFvkbAgM3TI737i0trVqTkUotcuFKLr7vumXkY2+ylUZVbIuS6PDXPxDo6f6KtPWpVI71H15KJFd6vjYU0J1dDg2uuAaIdmo86b23DVn5Z6Gj2N5JNm1fr5MuYqt9p67QXUGzPfM0eQfCpvCaLASo7hyPj50FwUHAdqJFJ6E4pw6aPruBerlVR5uzQ827dW1oQWDwpw0lhcZVyRu8D6qnzvMhCJ6X4uDYdD7h/P/DTb8had5NfpOOq2f+GKaKjTcUkCsMDLnX3kEs3lv8XFtM+i8CTTaYCz/0rwYC9jdWd6zOLtKZ4DrShoPFMUV1VlKuq8GCaUUgMvpZQn5X03gg3cgK14vmRpeHZmTG7bWnJxHOO61nACVCwJdQxQevieUv1Q7aIyo2xYBOpZf2BHSYgwcdaVgllML/TqU5IQZdUEIWl8dRwnR4TU5EX98VQjxOYLuXgasle5OW72ViqZz2hHA02huk5Npj4BkLQpFRDTLGh6TAdKb/b+Zjb2pfYnF3b9SRPpybtLvarUs8fsXu0gUwq12TTTh1HhB3JM0gFI6a+CTxiMHIALQpVC2SJqrNAumDQZ43boEFTxd4Qzz8c9+WgLefXbSqmnSWDYzQWHQYTHxrPbKC+sCNJ91hiZomjGDwDDGNhBe6YP/T4VHsC+Bd/3aMQQAcL0xOIALuQtKEgDjy2slbpaEwyX2I+sYJUUWucc1JQeL8AkYRr/WoZxPCH3Ou4sBawCT67yNKSy0wmy7k8X+/QSBix8mHO9TSuP77bNuzL5XJXndVrRtdTiK2nn5gtc/bnD7PrtagHAemZSNrB68JOglU2rwspkSBBqafHeFJSb3n3lx+JbL+wSB111P6PVUZoGUlJjAH/wAMfartwGAyuJiaBLV3mnVB4+pVXQzaCaGFMNcDlIWsIyZVL50O7+/9AtS8bqZA0417GcRaApGAZcscpw3ny91zoN0JYwuxYyGoUKXIx4+k5qZmYJY6w43FDg5NyWQ1TNUR9YIuKWQ9LUXdmE4V6y86q/1EU7brzhCxSMpYTwUlwtowi2GdutAwkF4iWRIVDQFxMcKs+e7qFVI5/Y3bLsxhwWCCLw/k7HWgMo59GxmCcDZPF12qHpgMtxn8ZNCnt0YyvQp698QU6ycAAerxLobp69IXE6u17GASvS3mxcSuz/Pn34jiItdspeukOHOxOYHotqMRBtHprl74IC4jzRkrnfn3XewPg6p/Texbb+kJsqfQs+2uFRDz04JA/bFicvSzXcq0iUoKd7qcErJwMKZm8uaB9czbxFDbV5VrB0Wu6KT1b9e/k5Ve3JzznhIxd5asvyYH0EWkpK21N9T15Wg1YljYSpVgUoZ6tnn7NXRnTfn1kDx1F4z4vB0UuRDgRkchWyUO3v+ayTB+Dv29K3fZpeELjDzyTgCSCkQTiqT30voDJTVwB30WpNKhWKUdVxtIrIk5HmjiBodBg0f3+LCrkcQeDNI5w6mwI3+8R6Kf8iKUjHoLLhvsS+PzHesbBAa79UCwBlROsoD5DwIWk/b2jtAUdLProZzGkvBI83nqD0jG/U+k8aklNTv+UHVzr80wzE13B07nRUIu7nN+fL9Q4pBcfOXQ9ZWl5XuSxWg4V6Wt6+d1Nl1bLPBLBBqPZJBjD5VQOK2QnnQ5oxdVI/UA583rlsxuMLKbGr5YlSapKIF1aFQ02Xve8d66JKmrlzbwSstHfXn+/FkYQKgc0QTC1uJi3q3KV68oO0FSxCVnUsd+9LvO/p4kjbPiwF6yv0u9jH4KkJT697NfcHrLpirY3NycsTYI3Ezw+BXyCi5oAnpdnki2bFe7x41sejOuI7Wuy+AVjyeB8Ym7M0ElN5RoZ9nNmtJy8Lq33Qwc6h6hW9Mo6tOniHp3/L2rG+27MEJSrRZKHRD3QkjJpwMJ8Wiaewfe9FO99nNJDVNZ3Gl9E1lXF23jORDrug1rCs7V9+fRReT95Vud+c25Jk2x2L8QaDzySO0/Pnz3eTdSeaXLxfbiUq683Ipkwfx19nSSS50xIUe1HWzSFow1T2Pp12ZqF51HWCPJJeZC5/huWy+PHrhSS/ha4wg+jmYqk9STQd5xKFMMQueTj+RU3uRY+xq6N9Q9eKrsCfMNU0f7aQCXO3t0qNJRUmkiQDRbiT04OkICPJnUw+2VIxBzLpGBHZ9Asttrsu8J3m9Pe8hQm36IwE413GBC9kBM1OaswPDTBcqCN/1ND9260R2Y/IbkgYh/MrYQTXM9TLOyJCJiNfjYIy36m7vYNUCIhTScuLG/ME9fjSX+hwAo8l+F9TS6U+QgtC+6nN4js4n59eWqaeAJR+HmddhyTpgoHRtv5ga4Mra2E99q4Nieo8Rer51fEDqaXCQn/mYmnKPB3qXdXfxGK0KPEDUmW94GMoAxbRoNzszi5zwvsxaNcinmnPUSlFIaM2zruj0Ec8/CQi+DzqFI8MkHR9OVYBaRRIyTNw7rozTaQPohuoO6+tLGvom1pw6Ybv3Wf45Gzm9UYgKCEnIDT03USEItHeb8EYHrPf0kzaKRbHUMi9FIK8ywWZdkqStbU5bsIyzmLlldgblpQ8D10U+oIjfd7k0WzI0ud7z+JnlP7QqWmwmJiMg40q0pm9js0mWriF4TO109ROvbN+uXcjhR2+LcqjOFPW78YsZyrOXn30yn3nXtSnfKbvKRHKvQU9eO2rJEs/rCSDGPdAx7YViPQ262Z1xyqc4XDUFzpUtGJNUYHKJzDzDLmwWoB9DIdiYCC3GHWLa03VJDWgy7wQeHcgvNTQLpxZswjn0TK81+Oq7WNEK/klHOyAIzY5MvOGrKnjT3fJixw/KZhQBzpx8STHATydXb1NBd4c5l4x6uJmBNV/GFvLi+Q3cPRHjR/9+Z0yxd1l12rE9bD95geYkXDWS7BIFsapq+qmPN6pKx50J37nrpC3vleRWHgbrazrWZyd7cHuxPBxzIpaPFWpEcxbXhGWEqvr5lfL1NIYiPC+CHyEEZqMlH0rebm3pq0RSGVVueNmAJjPdsWI9D/uGmwsHYg4mcouWYBQvOee0eMmCL041ovnxyd3Wa4jOAA9ayG1zPpe2uNuHGMZ7opNld0FlzevKbJ5AnWh7kNyUI9JZWNokd0Vg58kFpuYvr2NV8rjV+Q7qFK8KkHyMAm7ZWLO88kK4S8ULerguqAinjRpdDFFsnLKdOqoDpFfGCgHaIKlf2UAvLqbTDkutNisqHpWV7SMY6qcH4f9fZazedzcyqggVm7GJnvZD9lUbWrXFvlAM5sZkTkq4ua9rAq1XjlppSMPE7P2kS0ABveW+UD7dJ95Hd9qES8E9ABliDZ5G6mAIkpa788hfmp0nyG6GBEApHF2FP2OndPt6sd7FpQ98Nk8DFvJ2dAIH0cL6LygimZ51VG22iTsPgv6G++sm66F3AUnvjkHV6fkwgVedjOVh9siRGvmNFAObFW+saBYVN49LD5WC3cFtRwZqOQysPStodgt0KSFGRtKWrQY9sKt2gHIumAwHn8cPX/u2kZgUabOx6JfoZQ3EeefjwoVzxtz/AmkTlclZUUdc0v0+L4Zwuv8MJT8CVCxu58LvKiCWqXVakrjUr39R7xag2aeG6kBrzC9ikMvhVUWppuJ1epCY6GWiFsuWLdkMY9xzYoj5hqxyJP/3ViqES0nckedZ+m/5l2x69bOCIBvuV0z3S/LGRj13fXC5hc9UjctYwYT5p7Z0iZzHqNY2aneO0XTEfu+mp55p4e21jbd5bwVg7r4iZ55+R+4iwVjTMe4fAGz8VplwUan6zF7FUPi1N3CuGFr8sLCrTl8ic6Ar1fR00a7AVDOEamp3GppOE7lGTLPOf7kT4D0w2qZ1uwSa5f3qSyu42g05+UX1Jux/7DxOAzp0uN2CDRYjA+BDQ63JQbO1PJ3KKXOlAUTlK93B12gAAIABJREFUmZQyWrRNdTl/lolEK/260XNQ9a1VtbGr1EgMZ1MNETzE38lNDDWlFNBuPqpQumYpN2KrKSlnfj87da06p4x7DIVUE2NunAjodfk4Gc6d5nNVl/vyhJkudxP75gsvpEUExoQAaOu2kCR5gbiGh8F+anSN+DdRpWOdgY6OSVBWe4u9sRGDOzs7MBOWmDNipoa90cHQZAuBA9QN4vAZI3fdaUAQsp52YGWeDDhjzfMeOCJ75aYM+8NCRAARmFEIoF0+ox7XzBdWb5fP/HvCO0AEPCCAdrkHgMyr0S43x0aqYXa5m9g3iQ5/EQFEABFABBABRMCPEUBd7scPB0VDBBABRAARQAS8QAB1uRcgIQkigAggAogAIuDHCKAu9+OHg6IhAogAIoAIIAJeIIC63AuQkAQRQAQQAUQAEfBjBFCX+/HDQdEQAUQAEUAEEAEvEEBd7gVISIIIIAKIACKACPgxAqjL/fjhzDnRRkdMdykf7awdnig8+ju8YwX70kzLpuXQb/bVfvd3OwSnsOOFCCACiICIAO77hq+C/yAw2m5zXDYXp2XQdIdz80baGnFfWKMtYDWkI+3fDHWPuDkgXENvlIXtV12LjXap6y9ru7PBePc62i8ZaasNszzdV1I4aMRWKPN58zhzVliDCCACMxsB1OUz+/nNPumNd0ilW7feS9QcRCbdvLtt2yUa+Rf4797OThAfCjI+2FQkHe27MELPOjPqVL2HPN0v3WQIAuo29s6BhpJuuX8ucXMg7IjulHQCR8aNiPu9w4kZymbpqmEB2/s2zMIxw+QsQ6C/vHQgz+Y/Z5zMMnhn3+2Y6nKTo72054PNPkTwjqYPgYDF0eS2790bbNsunm6yIMvkuBQSHxonduRGExPS3V7SaCZQAzkonO0WlAJnmwpUMOb4DUk78kQ7d1KL5Uia1JfMim6ffvsFvSKHQca3l0lIVmYAJQ1ewB+sKjfGxCxHQDzc7LWTeV7fqHBa2uqipjqbxetGSOj/CNTvWpVRKYmZmN/T+EqElNP8muryiK2nH6RUJSUdXfnhtffkg7rh5NCk5df5Eg0/zCIC40PgcmGDmY3rA+OOq6cqSWzqvZrstmQjFzfHitPESql0VImRUS5TsXl39Znlcp2YgAiAgEBfFHL/l4NglFtkPt3q01lsDS1SVXCulMLfWYZASqmrLebFxC4fbis8MgrGgT40QFL/R+BC3sKdV45+9vCucGA5PQU1MpWYqXNTXW58o+vsXfaemHer8tflmI0OjBt6KnVW2OtS7DvDPNFh/WxHwI2P3dtbp6eZ3QMVHtbedjnVGhdMLW/JhvaOR0dfS3dI1hH3eppY4mUC6cB1xv5UNv25nD3IcoGeBhOMSvhvaKCtMTCqlBnlQokbHzvXDpNzHoG0wlv3CycHhf5yW3NqaW745HBHriYIOI9XvA+G+A5BkQPRM7vK8s/GFRc1vqI9v5xx8DmOPcIaSS411PWZ9D/WYmdPz1ibYjtEgENAUuTcXDi1vK1fNpRkN5R4FZc+3FQIxnGYheMqJocGmmpH9cVCCZ2JL40JBu1bnRALfvLqtC25gYQOJsxaaMv7z3VB1NuNdtMutA0wjwhMPgIDN65OfifYgxaBnu6vSFufSi9a/+GlRHKl52stKcv7aJcb8hh/YbM98zR5ByKS8JrTCIze7ibj8bFLJ5EnWnQwinPqoOmzGwhVtCv0NEKj/jIWyFbpKJGnqVTcusjTwjS5qlTMDN0bWvpkoK4GvPGcEa+rFgqYUR4cPUIGYb1ZAGUC3MgCE2osRgSmBIEm26tV5NC/Tklf2AmHQGT0s4T8rqbxQHoqV2qe9FmXO109ZFVahuwM76Nz6l/IHWwqf/B2kpiDyfXNZ4X0NjbFrsTTrdrfVUO99OffXA5aXLj2Ji3fy1Jr7fWOraE0KXJ4qXbQHlGxKcbeyeqN4u+4voiPMjCe+J+/IGDmY29qX2JxJyN1pA/nJu2u5hzUevr4FbtLF5yxddVkE2N13nG1BoLdzMPXT114KiFez1cpCQ4BAXSG9ZftJToHaKzSiKb6z30TVRpLfuO4/VyQMhownS8H173bO1Uzx5wfItBfvi7lUKsoWE6NqzhNLyRHs+ZQW7OBo1uIemMt4wuufJJnVTERw+hYmWEXXHNCXjt5qzRZat+SPx+0uHAVJM4vYCmDLiR6/J1YBCJ2nHy4Q83S9d9n2559qUz2uqtqfdPlgjLe9uFpUMP0ohq05x3HNYek2kE3J1VImnid/cGgnZCBY1np15naLoysfzAYypj0OgkBJuvfvvbgbWDkeD2kbJnjtHa+HDg4wpOSes+9ufwKawuktPmeqgw2FICsWGKPrB28tp5lQYZ5WeJYgXghA2uE//kDAtQZbixHcGhKJquhi9O6SG7SRiHSm5XBCrFTlRCyLgWTSyxGBo32UwkO3Vj9BCz7vjNELBrvNzAvJMm5gS2DEgsff/u/vBe1wUCXB4YEBufGcjLD/HrfIn5M0HHV9VxiSvCoMFwVu4WRR/UKSQTVmjSpEH9nKgJMU4LubGa601WZsdKaoY1C/zx//o2oK65bVD1TpZ5oC+d0rXjn4bZPbtkgDQxL1WCwccCKk7fuC+oZCDRdCAOFlz++/wlbhkGzGaVyJHxy8X1XMXBsOLjkaIThMELdHeYmG4H6Y8Xk6Ge71KM1uVPPuvzE5uUnZHICJvJpQWVCmdZGB/UcGfdFbYtzqy4y7rwdFLlgbdMI+a0KRy9SZ8n6a451ImFEStpaO0zY54iKv68qlypyuyzV+rfr38lKL27OUcLvhabjksELMZFkDAjQ6W3fNWel4wyR1HnH1QYSa2KOByZvkMPTeOFg0KArF5aTge6sbWvhaX1ID7tuPpUgjg8Ga4TYN2P/WEDcEVlJQwfDTV8+mbJd1RMMUOC+OPWvqoUMG8EQHwLrtAwwP30IUMc1bwRbc+tqnEuyTjTZCplaZZJ9AIpctrMteRWHzq0sLd+TrLG8zW6it3TfIVDkip2dXHzlUMZKpQtK0AqKnOuRkItdA4RYzHhi+TQi0Hgkgxx7qITCaUXxrMsF97i2HcsrWpl3ca8yMK1OkJQHgtvckJGHwpc2SIqcEoaFreTonU0NX6xKi+BKCAmNiiZ7zzveWyd5+1nt+GRQdYCZCUOANz3Fxdniqmuqqy48tcVgNxV15/ErNkoGrkb/WbarJ86NbHqRF9c1teUbzZeVRz+l7l7JXS78Fvz2zD0urnOjt+DVQCVIo8gFpkP6OXt+TVp0IIlesEjpH1MzBoGWug9ITo3szWZyp20rWJNSXLotRV4g/ppNpbat4ctIxw1waJrYZeq772+s7lidHaoqpBwK6hoKU+gnmhEUFSlDB2LJa3blqRpgxl8QaDzySO0/Pnz3eTfyeNblbhpDlTjhDfPfg9dAoVIHeK1Bi23rVWrVgGKsRc6eTnKpMybkqJbBJm3B5Mmg7QnzY0MAQr26B0+VPWG41RpjOdrfASvB3M0TG+g/WZhottJbjiyTy8E38OkC1aDB3Xy53EybiD24wqItG19eJYboY1/UPhCYyYY7TGyfFq+PTxpsPVEIuHqvk/gNagMErOGIFe7N4tCoNeRcTz9Js3ghyMCNVnKxNWXJPi1tjlhACZbt94aVlgPmpxgBUOS/CutpdKfIQaJx6XLQ3JmnaWCa7N+e4nsUu5Mi6aand+x1ghCAbVK0QWeayC/Wkbi/qVmnyoJsPQVbaM5HllGS0c4/Prnb01JyPS99iXEEvrGPXd/a2xJLAoGTVxLN4/C9ZYR004aA03mRkA2T3z1uAzf5GE96D66PfkIVuel2b7IA49HljmJ7J8TBTa8ihxl60i3fDiZmLgLDLhpArpbfSDGPdAyLS7bUtGPNBcRxYXQikzH52PUR+O597CO1AyOZoRYv5R76bpiMXGY+9uDowJoDA1te8LIlkvkbAhERq8kNnVD9zqtE6xVXEflkSVMjHkKOzS+frHxzNlgzqQhcyIvrO3T3gNaHY9Snz3vFGDGZ6LK+qmPN3vKkoXD6vWt84eBtT0g3mQiM1Loug+Z+7tuSsmH3/QTGc0u23JOOuRac29Vp+n9075cpvtiCeLrLDWzzLom08Y2ngrvvGcXoT7Fw2N3YELDm5r/WcejX6hBLV/O51vgN6RaF5dXeXiUD8eSfV5GXMwyikXgiOW1JzY6/WN2s4kBIb2llk0hiTEAaKstdMhODBMfBoBaLJhIB2LS1YnnngXQVzwt5uy6oCqTMeHT50mWryIl3qyAYQ7hg7lxaAu44VgHhkN5fwKrzkyahycCxPb1RfLCbezZhOZV2sjdp07E+iQ6WvO8hGd5zkNrh7/QhAIeDkeQ3QgMhFA7UeXZD0xzYWnpk8BtYF+fhorGBbFTBxwDSZXUTPTfvQQ6snlAEUkpP5nzwakZpv8iVrkkrIEVFqmC31gKbTABLzrI+Xl20jQtV8yBQuK2ogBQkrquU1TksJbeRdTKHcJstp7Ug0daiMILlZ78PV8kAdeBFaK1rFBS8q9LWFS5zUBpiauIRcB4/fO3fTmpWoMHGrleinzHszNTHzlaB08WuX8CaNH73FYVN6M6aepKVLsedQcT7g+1065iYELqdi7TTC2sgLmwzm1wP3fnr/Z8kpc+zAzFtK/rtpfD4zJCzYji9VEK4I15oOL2VHvoibDVD+OlzmR4Ye5ABKPCaHgRgqzXYL13c65Rqr7DOAw2nYOrEPGjcVFCjKXaeWLNDC181zrT7+fKhC7fAo87Z9aN3btLtWg08/OOUA5vPBARg9XYTrOeWYtNgDxaXWolGwcYvqfUvLpkvDmthp5c6b41yAQAalx5hs0rbvBDd9DmVIQpkmC8Bxi+Tk8qINbe0qC5xpfUQlND9atTh9zIZJiYWga//87Ov3m9b9b6O6y+qjXX5Iw8fPpSJ//YHtxMsMh0mEIGxIvDLtj2apjCp3P50Yoq0royvdXcwuSrGW2xE56fdLWNjsW/iQaV8P6o07ZSojy3nF8Eb9UsD6OgZptq9Xdl8uZWF5dOuL6v6YRltQIDCh2urbuZZGDU95vwAgTd+WjPJUlDL3pmvXiw+yV1ODfvwH+Bu8J6Q/mECUJja5Z5aYz0iMAEIgOJsC0mSF4hrOBocTE7XiMNep4neH1ii4hkd434HVoGY7cPKtRMWwVMlSrJMT0ENsRoMR56S6MUV5xxTwyTdkV64jDetgzpBGAEH451wDDlj4exCgO29GiXt9NJfvhVc9E3o/Z5dD9mnu0G73Ce4kHi8COjt8vFyxPaIgN8jMAl2OZtih7VtwmW417pUOaN/0S73/PjQLveMEVIgAogAIuCPCMC2r/dz/VEwlGl6EBhPHPv0SIy9IgKIACKACCACiACPAOpyHg1MIwKIACKACCACMw8B1OUz75mhxIgAIoAIIAKIAI8A6nIeDUwjAogAIoAIIAIzDwHU5TPvmaHEiAAigAggAogAjwDqch4NTCMCiAAigAggAjMPAdTlM++ZzV6JR0dM9ycf7az1cOaK97D0d3jHCvZjOTAwjiNMYBO3hhLfOAzDAe1eXR1X58KW9V5BgUSIACKA+77hO+BPCIy22xwGG51KIrYMqrdWlcp9+hX3hTXeilXFaaT9m6HukVMHyBb+XBMViftMQNyRtEVlDaey72V5fRSKqxB2kPXyurd4zPvfedkDkiECiMAMQWCW7uHaXHXMkrMzbIY8BBSTQ0B/CjitpFuW3ks02T/V3bbtHGchCfx3byekY6BzKMjtRrCjfRdGiInKN95DHngGh2p4WjbEBN8k3JEqkkBDIEBonMG2r8QYAamd8Cvs1q7pS02CuZmNQH956UCeDQ8ymdlPcQqlN9fl/AljokBmp5xNobyeupKOd4PD1nI80WK9vyEQsDia3PZdKINt2wmh2q5ygalBHB8aJ3ZkcuqJUNvdXtJoJlADnO3GHwnT/2VXS2MXd4Kk0vBUdpeSUVJdt9UclBpvUje/GyGTf5S7N5IgzcQi0GSzvvwBIa+dzPOaL9uevUN3EprX7ZHQTxGo37Uqo1KSLTG/p/GVCCmn+TXX5evsDxzhSUkNLzpOCwYuVZMhy9fa6x1bQzVc/CdLzz9NgXNXG/xHJJTEJwSMTw6lLEJ84NNx9VQliU29V5PdluzBEW146gk9r6xlqQeXPpt3DxKl6rha0xjoqS/5DmAA8a21ekXg0CghAXIpSwQOf9oAh7h4vlKtBua+52ZI4fcIpJS62mJeTDQc/5kIHx4ZBc4mk0osnpkIXMhbuPPK0c8e3hUOOf363dR/ikwlZurcXJfr7h7UZBfZFGM/cCxF1O46Ev8oCAtbOTmCOCvsdSl2dN1PDroiV2MPM/Oxe9svPdDsHqjVsPa2y6nWuGCvjjpVMe/oa+kOyToi6WlVnZKxxMsEo52fDgbnJnl0esN0gOu5tIQ/fgeK3AKcgjWKnDKPeiNtYzBNGF7M3zBC4LBUkxkHw1ZYONsRSCu8dd+bEeAYcOgvtzWnluaGj6EpNhk7As7jFe+DIb5DPq38mV1l+WfjiosaXylPNWDrgy6H1hFbj7xTm763zLHz7SQDZrO9yNnTQ/BUQT9/ypIiB7UqhaBTyxuUKDV2teeFG97McFMhVcwWfeXQQFP7kpRMvQKmYW7gtBcVrb4hX9LYRmPW+BJ9mo5duoyD+uEWqhMDQZLaUSNJ9LywBBEYFwIDN64SI+UxLqbY2BMCPd1fkbaIHlC7MqX1H15KLD7b8zVJlRW8XOfz+eWhUdGEnG46/3bSeoXJ3Eg12zNPk3cgbAqvyUKAnt49Hh87C4ILyapOtOgkFOfUQdNnw/wL0DCzWEcGBf1l7TSWvtJRIk9Tqci6yNOqaXK+MjAzcXemWOBhwp5vpk8Hh26s5meyqM//9gtcv8GhKVJH+tZYgghMGAJNtleryKF/nTB+yMhLBCKjnyXkdzWNB9K9G0j5vL48IhIsip4bfZw8ECUXslz896ZDroD5dVZoP0/I+TclghCa1V4mHIhYTptI3IDPpmN874yX+1pKMo4uROE3nyWkc2+SeCNJFQOau9DI4Gx2ODUUmPUCARpnXq37VxoTmxtmcdccHOkNbSFJu82VNG0dv2J3aUwwGazJvtpvyI1OexMavq6XoTptS24gWPYJRsHnGmYwqjh14ams3HsuzRwmG0x4tTQcKM3WpoPVnt1wphbm2vGawQj0l6+zLpkv/ss3DvHhaNZV9hrdLES9SUxeLHdpKSCMTqq1GnbBNbcusfGRmy35TDYahddakCjKadCFtkvMTwwCETtOPrx7SeVOd/332bZnX0ozMMqhS991uTWSl5QqsM2kdvDaA/avluTNy6oSdBgNQ3PsX0t6CrOWn1svEnTZezLV6twNB0Lj74ADOffm8lxyROiiy0727hG7ECQBXRtjj5RkOEL25J3gRRTGASZCetPF+rcF4cu3EQiPF29EEwAIdxHTs12Q8MEgyLA8ZnMT6nL1c/CYY2FohpqSmqHMs22kxsACLqGhZGkb1d7vkUHJy873TE3ehFhy747ehQ3MC0kyKOzxXExCmBTffSTUkhm7+NMGWXMzPz9M5KdZiXeb1XR3ncpuAEdCSbajhXksWLqh5DffgGshUX2z4xEZ2049AqApUw6tOHnrvov+u3LoepY1o7RfLcfn+fP3kQpGcL+pgBQkqnStSBtu+4QxOalbuEPHAS8TqYv7J4m2C0qQuC/qY0EG6OLqq5wMycVCec3LZM2hNpHmkzyrWkbMTR0C9ceKydG3dpk8Ad/my7VS91XlUiVql/3t69+ufycrvbg55711Mm3nyl3X5Kww4575ZsoDYcbdKw5nyfprDolhREraWntDXZ+0fLyvqvA0qFhZhtCdNeXXQ8rk7sn4u1B4maUG6mo7t+1KkqpBhmsZECjXR9aHSWX4a4gAnd4eNKxxV1jpOEOSRM3dcbWBxO6u1s9hA4PA5A1yeBrPDwYNunLQwb8hadUrSG0bb53wzTyl2fI2Op+dJgwH+ssgGD4ktrCts9R629Y+nAtuAyZnsK53gfXQd6DkF8vdKLP7Gh87DYOXqTAx8xCgjuvXTt4qTRZFt+bW1TiXZJ1oshUqETkf3Ii6IutOS17FoXMrS8v3JHupTXtL99GxgtwFSS6+cihjpdIFJWh9+eP7XI+EXOwaIMQy8wCdAxI3Hskgxx4qoXDaWx6XLnc2NXyxKk2ZmafM6YT63vOO99bJiu2lDev4XkMzMuP21vY5SRI0HAsHdZi6wKEyjO9ClR5/Fyp2xhn9XUOcoH2nMTGWcgiA0xvUp3AJ2lTaZI3ON194yvOea/ErNkqmPDQBvS5b55bt6olz4G/rIrnSIICTgu5FA4qcdU1t+UbzZeXRT/HtxLQ4IoFp+DTpU6ysahsJaTtl+zarOmlx2a0REurJ6l+wSA5ip3Z5l9JdYQO3KV7g4iFikSkVIkzNAARa6j4gOTWSIhcETttWsCaluHRbis0i3sFrNpXatoYvIx03wNlnYpep77u/sbpjdXaoqpByKKhrKExJg2JGUFQkva9QYslrduWpGmDGXxBoPPJI7T8+fPd5N/L4rMudLgisEy9nTye51BkTclQqkH43SQmz30u98EpSXT5mDhJnyiF6u3o8IdWx3/F3oWJnkgE/fC2NCaDVfr4E3+QO/KB46N5Q9+Cpsid2m662GoXtyi3xhia4KP+QacwaBLEHUt03ODpCAlQKFTTxpwtUgwbz7d5OXTACih+RQD0dNHwTVZq2m/bH9o8jC2AdeVjIvb4h4m7dGiAgs1fx1NjljKhjuD84yCLTY2LGIODqvU7iN2g/WpaIFe7N4tCoNeRcTz9J8+ahD9xoJRdbU5bs06IiueIpwbL93rDScsD8FCMAivxXYT2N7hQ5SOS7LgfduWp/hmwHr9rfVZOjfSt9utXxc/DY3RR0QQidVn+bBdltTp9nR43u8aloCfq/HNTumaoxTFkLWC0mW95aFpBX/NL6SrbQ/DnNXmmjnX98crenpeR6XiYlzBwnbNkYpRCs85DYbjo9b0lYcOM3A2GS40HPYeSP90iq1UJj6RtoCJ7mUtnlQp27gHxNa8z6DQJO50VCNky+OLgN3ORjPOk9uD76CVXkptu9yQL4qssd504TsilMUN40pr1bZuVtghrKkmd+bBz4nigHyWPPl8vp8Xchs/IqAfF6g3aYpE9KSn/dqgQKeNV2ThMNu0B7aVZfGCnmkY5hrWE9LtwC4vRBZL762KkATG13w9ZvgjkOJWz6nJr4AZ03HbfB4o4PTVza4MbxAMF6wSHU6xAYEhicq0wWCMzZmjQ4L240LpNOt4O+bwsJs0AKrxmGQETEanJDJ3O/8yrResVVRD5Z0tSIv65qrsn4ZOVr2mJ2qhC4kBfXd+juAW+sZd/i2J0VZScglnu7OBdOw9AuQRia+r76qo418yXqBWyEjgbWZiaLowGvOPDctOkIiKtnHnulornphJIh3gnJNfAyyd9mX9XrmiVqYTkHN5ErrgEvmSHZSK3rMmju574tKfMQ4x0YrzGsJwE8N2vSjHsDtX2ZvAFL6RJFFzq42bPbYcN2zXyBZXtCLAwUjBebwWgmMCrB3QwCOOrjnv7WpLmxZFjqfwhYc/Nf6zj06xaVZK7mc63xG9ItSuHVXtUitIbPq8jLGXSq25vLkpodf7G6WcWBkN7SyiaxtTEBaajUL2zj++M48MWYngQEYNPWiuWdB9JVrC/k7TKc5/NhTdrAsSxY+tW57UNuA9ewnEpYIZbELfgGe3QPyVjHdw5rsuU15cAk78Sq/ZXyju5eceC56dLr7LWbzmZKC+GoQfxuz1q2EPx1YUgx/i7EPpcuW9X5SZOgngeO7emNkm8zLIzY01Urzll0/Ysp6tATnexYICEw3F5Jkt8IDYRJYlDn2co6LonAz38hMF7S4sxiLqEh8dzWLor4QJkQKy02U60R7/iW7TirkDLLXlqTtjTB+iVLf0mgebtm2TrfCNP+j0BK6cmcD7gFYK7KjJUFpKhIFezWWmBTVqm15Gd9vLpoGxeq5uEuw21FdBkbtyodlpLbyDqZQ7jNlgMLx/l1bg0Hl/w+XCUDdAJehNa6RhfrzlVp6wqXOXiQAKvHhYDz+OFr/3ZSswINNna9Ev2MIV9zHztsrkJ3RyFfJC3fKzSFWedB7dQ4XURutc/jabTT53C6Wsq5kOWZMhM1gTsOkgyZIWe3fcj81VIJSVp+XShhE9UQdyaG4NGp8e3FbxIHt8vs+Ltgsofu/PX+T5LoXDihC81PyyvxIL/MXp9PDswL6YQ0uygB7twuoeHhF7ZaAxNWtGhpzFdY54GGUzB9Yxg07p6Z0RQ73yKWz0x0WpjnFo9VlZkP3brRHRhF4+CEiy6jT6i92pewgo+D6//yXvIGKaSfo+S+nOy0VhgrPPekJZ70fynxw98ZiACs3m4qXyfHpsUXXHGplWhUwZVPUuthHxhx2JZT46rz1igX8KBx6RE2a+L8AiGvmz6nMkSBDPMl/PhlclIZseaWFtUlrrQeghJYaN6cLNdgYhIR+Po/P/vq/bZV7+u6+EW1sS5/5OHDhzLx3/7gdoJFpvM+QaeNew9yC9C9b4qUsxKBX7bt0dwXLCRrfzqRPzxUJjAOAROqjULNPS1jY7Fvno4ZpZ0S9Qlp/CJ4o37FqXHCHz3OJsvlOzGa+JcraUK907tmcZ2KkhPGQxigqhlmphOBN35aM8ndU8vema9eLD7JXU4N+/AfXJ2ajmZwLz9MAOHN7fIZfGco+oxBgAVwJckLxDVyGxxMLi73UhzamiYest7twCoEoCmshOVhVImSLIP1chD1Rned4wxoaEqN7xQhIA7C2s1j11kvo53nSMJ2ZaZ8ZJBEbVCyiiSQYsIIo5Y0fdSeihQzsxcBdmA5bNkm7PTSX74VXPRN6le8cXr6AAAgAElEQVRw9t483pkBApNol9NtTe2izxmXXBtgPyeL9Hb5nIQBb3puITAJdjmbYoe1bcIFHvhi3zzwUkt//0W73PMTmmy7nM5Sb/UsBlIgAogAIoAI+IgAbPt6P9fHNkg+ixHwbU3aLAYCbw0RQAQQAUQAEZihCKAun6EPDsVGBBABRAARQAREBFCX46uACCACiAAigAjMbARQl8/s54fSIwKIACKACCACqMvxHUAEEAFEABFABGY2AqjLZ/bzQ+kRAUQAEUAEEAHU5fgOIAITjABs5GJwQgzscmN8pMoE947sEAFEYA4igLp8Dj50/71l2AYODlYxUITuRYYd2TydrqYwoCeYNagONVHqIAV7r7Z1wvmkRhdV0ma1sAlr7ajSqNHFmMCWcCK3kfZvhmCveO/lVHhhChFABBABDwjgHq4eAMLqqURA3LQVdHN2O5xQYrhJu7E8bk4c1zSIDgwmgUFPa0r57EiLraGFL+DT0SGL+KycDg61DsIoRNrIPdVKz03p6GshC2Lbh0lmEJxNToz3cpdZYAIRQAQQgTEigLp8jMBhs0lEALYcL11wxna1v3qFxctuPB5eovABy9u1ONhkt3NKFphcarzfO9sFfQGMBQwvy4aYYJurc0NQmFg92vkpyToi3MLonZsk9oUgw4ZYOHcRaLJZX/6ATOYOrC3581+tEgCmp5zlhpuADZIUxzTV2Swm9Vg8wQi4PvpJXLHBUeSJ+T2Nr0SIncER5v+0u03M/KL6UnmqqRToYzeFBiumE4HgJVHRg22819oXaUDp6rzow02TPV0NMqc+FSZp+pHaPvLGCsvQ6AhIDseekpiEeNU99HcMq/KYmasIxEdJn+4JRgDOI59fGnXFdes+/Guix5nzp5XznTUchCEFXlOJQG/fhdxjD+9e4v59VpL4bEmZrMgv5C08TMokgs78K9mrfnL8azMZvbLLnRX2uhQ7nsZtBiKWTwICo7e7yRC5NZIZGugld+7YUHBoDzVe7nz6KUJC4wQN2vHt5e7B4dolGzO9ZOcLGXcOaUtjF2vZfgp+Kgeprz46Jut5mCwfOZUtVLF64T/0unNgzMVkSqnrVul4bry/3NacWmpobbfkZ92AQ9ClY9EHbrRCR729JFlnmrfkHyU5a8hEH3k9nvua9W2dPWF17z6vus3GD3cv3/rQKpY5j1e8TyLqpCyxvvLbo42R+z+s33EgXdVMzHiny3t6CJ6mZwQflk0WAqB6gXX3N31DoXTi2ZsrODSBgDkeuzFz1NUIR4knghbvL2vrDKYO85E/3oPp6o30kFAuQs2Yrfv5chgf6C7hUFRWTP3wleqpcXpO6wh31jgExDluPJ9EhWFWu7eDFV23WIAIDNy4Sgz9rr2lpVVrMtpkVUCSi++7io0Aa7KVRlUUka0foy43gmeSyiJ2yPa30MPX7/7KWVJ2QO4uIhK8Nb+raTyQLj1fViLXaxNe6PJme+Zp8s52bcuJyjsrqpxbc9Zz7PQlXCUm5wQC/V8OCuZsW/toHFXAXl2BmYmJZQ1NZSGXYfqcmeOW7bF3IJL8jVhyYcTr6Wr38+VuJem4eqpyQXIuaalsL2kESsYKflNjougYQrmRoKdZOjgAFblbQLHSLQJNNpgLP/SvBjT9jdUdq7OLdCa4jrThYHFMEVh/5boaLJhKBNRGOe059cDDu4pqhwJnj5MkpkaaSOVuvvz8m8vnhSyft/ksIZ17k1g6ZHlSxYCWVbOdkgn/3nQotWK5/TwIUbFJotl0rE8iYQQx9l4pT4imhOP8ejOlUvhkVTmVZmJDAxl4GkzPGASGqWH9Qqgl4Sly4Radb/b6smxPII2D0FbSkQFxbzx1w+Zo0U1Xe83Sa0LwtH+6YEv1Chr7Bs7z6rSs1JGWc8P9576J2hC6iHwn3QidPsBrjiMAIWnWJcK/dZXcJ5DCAmFotIrObfeXrzMkE5vTSe7WgkSR1YvlLhlVwaNOCBxzbt4R5f/7iFKMd5Nhm64EM8p3ql3uGlkaj0Tuj6hTwuI01cSdLl//9rUHg/CvfBuJe8chpK85tobyPKhy3UxqKRn9V0vy5sladp39gWP/WkLOvbk8lxwRCLrsZO8eSQ1LBApDTQlkKdv6d1ZREhhbCHy67HHkUq+sy93JoLDG1MxBgDrYQ6xgWEM0Gelq7/BF8qHvIKLs8qcDkuIEJqGJkpPKF0Y+0oIi//LJ3UfkMQRtLiyxuxMSS6cJBu+JIlEJAxdDCV5zGAHq8YaQtLYidUAkQwQm0Vn55/nz95EKJXLNVtovISY2v1XzMoHodMbq1n15ahxUeC84zJeR5oyVznyx9mQOaH117Ftv6Qmyx3CuXeoHf6cEATDKSer/VeZD+E4hlH3VIwtXPZJN6u4az5QL1O50+f/P3vvHRHltC8O7iclNsbfhNpg6DZWB4ZcgbwLYTvW8XK4hvKOEBM7FahtTUlo92N5RUe/5atTk8gcY+xl7rCVHbG3x0ph71NqDubzoHDLhEr620gokB1EQRqCd2zlGYkmj+I83fmvt59fez6+ZgRlE3E+I8zz7WXvttdcga6/fLD7z+/HWmvqM9mC9aiFf/+Glj8jho1SHVqacI+u1E0B6iWfNVV+HqporQOE/L9c3ZlySThLpW888VBeNiIbw6AXEguEApHIFHTWpTiQoIXVtIieYcdDugpIspKawmAz7tBh40PIhjyyOdVqwxA0I8u1SytlU37fL35bvyXT7/X+gPoLEZPKLVIImdC9EnvsHIcvtvsan5l1aRqblXj8nZZp4dpZWFfxwoUunwVvOHRv7gZDW34Mgb1RCnYqPXjv4yudNmu4eaPEO/x8lMs4Sk3gRfw5cav967W//N7jHza6Xd3XSUPaB1Ibn5xzHbrYAjo35fd+t9vAUpGRmkT2Xe06sK1JmbapYp9zCZ2rqKuYp8tvPSMlD3iQgzY2MhsjXEZCPmwNQX2UkufKQ7FpOLHfltfT5MP5cczZbkzgz/u1z7kNJiSSxu2V8ojzXSUCaBsgB92YHBKD1+V8NV38GlebpQbtaMcbYt5nECk+dKptpwPxg81JJtCcqcfiJL5Gb1PdPo/BcQJi4BAdsOfDu/1HEMILZSX0LNK8c2cZiIK51Fa81XLw0UYtG9YmTW8f2dtVYTBXD88eBwH80tPyvTQMvh1nR9eY3F8afqfq3jz1f7DLT4COIfbNeYWx0gFwdyE4+rAfZoh+Y+/O29erhgEM2nzRwC4uHuHBgyt8YhIpvTg15UmFN4umWwYFC8/otGiDc9Y/f3ZibDzeFyx0t9/BVaNIXdNE8NEhYH+7+arKwYCmO213JlRfwEGC8aK0Y43AC6es9BrHr7KXVoVMi6QpeTPoKUuyWjX877VgbybmERSfuBQei4kB6+itgY89wGif9MDwJ3h/i++zmvsZa42sxMt8cGPN1fkvSD5qJZz0ppf/0O/L1Od9Pu943EfxzkuW40up9w23VvGquJyDuzwuBhrhv8qlYYKK5bxCixngXYmJ5XvG3Pd2fTKby3mgDR2YGfn6xREofB0d7zVInKB8X77m3SxEeaK7vNimzxKHBqulZyxO5sfAPED9fJ62L6Wf33PJRAArM9YFJQMmpS3iBBMb7yc2RxMwdQpaH56qAmCsHboxOEPZYTPG9kk3/P3gayyDCrpJf4krJst8T8u4Xd5qK+RfiKX4cGB35q010Or/uyzlu8unIT4SYyPI5+cvTM1AFerzXQqDh8XJg0awOWm/bj9mqp5nZF8SiZzvCNSbBOmuFkscapibklyeR/qHAq5qGjRI3zGmAYNX0FUujleUqqRMXh5MOyCuCbR9S45habwn5G5/rbhwOSaXa1TniRnAg9hxAczqhKrglbixTI4fF0apwr5FXjvhxRAhyS57F4cVPN6+bYx374zvPPP/OxwH27U/XlXqu7Kh0H70sH2893iXjMQ9kYwCM64Ufmbj1XXggDSIuNGjoxd18cYBmZldayVpHiqcmkYDh2roOa2J5rqIByzRPkBej6M6CkzBKrrhCPRBEu/cp8mrRC1/RVm9VvtMtpHgHF9YuoXMkC6U8WsYK+Mg4cKupxS9DYqwcYSPdYDzQdfFKQcUGZ2TIBNS8cMBSPKO+rrs6/+tTQn5Xbp66FoksX7Fy9cB5PzhZ4Jo8vvtW5jplhdTqFsgxK2JSxsdbi3aTMhVAAbT8xFC4c41qzjpM/3h0DTlXnpwjJZRbTlRfzJ0GFZW4eVwcANM0zcx2WhMAWnUlpJaBdl7l80eWpeYsiE4qoyY9J6U5yVmQkH/IU9eUTSPhsH6cdvjAOq/3ig9kk5YeQ614622LN4IDdhwAv/iVjk5Jd8O49DQ12C3Ne6Thtf6Dqw4o0h2C3RrIkSM2gev2erwdGeLdbDkQmLxmMXXDzr1ryV+vq5kL0Iul6mtSc9yqvcozjx49UlH9z39bFPEDEVt0mKrLkGh+Rl+YHSq6YD0ZerGua2Z825fXT4CAN47AJA059b7/gdQ0p/Z8WIToGHiKHf7ZxKbAKYM8JEuDBiHuFgQH/rV3t54OqcSKlUbOQ0P2VxvWU4OLiVCLBgMY3lGmGjuhwXniE+KxJYPGvi1/2xYGKQRPgQwjucw9JQQWJXJIHRKAVe0UGGk74t/FzIEd/9im2x7Tvkx5o3ZLk5qn0eE3/kSTyowjyiRyq+l19+/p4dasDRoz0aobm4GSheUvT/u7IXWvi/EGGqjs/JTrjcbukr5VBiz7pP19IYBEJssVXOJTcGCOHOBlOYo6DHZTsrEjQt4/NVGQ5GRBI5LlWAW9e4ROM5GjQMltl2n4OoazDUuZ4TjZmlqpErux7TpKd8LvURLniI45keCjuBYnB4yyfHHuMw67WuyyPBYsE7I8FlwUOKLjAC/Lo5trBT0NzUMLIKc8ogs6jeps71QGP2eVh6YiRbCgy+zYQU8kUF+uhrZLUScohwCjdKcgUxP9YJNXocXNYuaAkOWz/naFLA/POiHLw/NIQMSaA/GQ5bGmUeATHIgxB4QsnzVDhSwPzzoqyyOJfQuPSkAIDggOCA4IDggOCA48Lg4IWf64OC/WFRwQHBAcEBwQHIgNB4Qsjw0fBRbBAcEBwQHBAcGBx8UBIcsfF+fFuoIDggOCA4IDggOx4YCQ5bHho8AiOCA4IDggOCA48Lg4IGT54+K8WFdwQHBAcEBwQHAgNhwQsjw2fBRYBAcEBwQHBAcEBx4XB4Qsf1ycF+saOTAzrdVX072dGWif0g3N+hHKxcx6biwmRrMXKDhj3U5mbsRMDbTPhMEQmpkOAxHmNRTY0SrSh4EVrwUHBAdmz4E59y+f/dJipuCAjgMzfd6eQd0Y89gd5CuhMq8iv5UruluXYg2Liq+4roD3D539OXVzudoDTS4Gp7zWf0axF2z2utSs3pweJz6HL2fLEpZICtX26mbYHDN9VfCNKIVmo64kPzP+LR4GxvtT8tkKd3BEcCREWKfPjCwxJjggOKDnwFxl+dip1rGt1ev1aMWz4MAsOWBe8RTrod5zW5Rtl8VzZAsC/rrtIPMmB0JJao/UqDDQdYZP7ydcc5SCXPf30BVN7ZiSVHIBWqroLywE+y3tzkJV3sjkWTRtWAty3/6593TVPbUkLa1Qy2rXIJhVwmwMISrlicVNckd2OpRs2ZdWnaHe9I/TAvjT3Y2+bnVQuTH/opW34nPiZNNkrbdYMEJwIDIOWMrysVNbsusHDEiYPmlyE7NN7VsNUPM9MHm8csOeq/KqtCfb5OUusn5dynwTIvFky8mHUp+3+V7+SV8v4YUscjf6TTi3U/HMT6QyzLrKekFKPgPPY8AuLDfX8sXVUSVVGp0xE9lbZ0W2wzvc15/CN01XerpkJeeR4NTaorpDVHePm2KaWLjc0TIc6M+Vir1Dr9i6coVMqrUzB4iERNqcVXkd28+Zga/M2sHRMvVJBzw8l2K79BOOTW5u9u4XtRFvROqW9soRf4fXGfEkAbjwOXBp1+qyFoVMy3ZqCGApy9O3nnlYgq1OV0ntSiVsIKuKcm5II+vqH/akFRWp7VWV9eb7s+e95Fry5fWHStP0yx/kLHmLrKm/tF4ZmWeK1mSsmOcVF9Nyg40+CzN7chTb7B863ULySu+1VfWatDeNAlE0oI6lSSTZJRmTQ1MTJMmJkhI7mudTAUYOeDYrpmZjixcAxfOHefsWjowwxxRHivvA0sfetWW6fbB7RWFdxf2zzVObGYPKxMXhUFa2R+EDtzHxIHGgpCnQm/26ezgKfqRlZIKxKYoJAnThcwAbnl47/OdHv75Maf3p49LfZpSS0c43081ot5TlZsCErKsfrh/N/rh177pqU3Tms+I5evmD2mv1l3oYsb3+w+vDGVuyR38kZN71cjjfBOvjud3Fj9vc9Ept7JFuHnVo7FCe2tc7WOrKd6CHGORo/BVBsKsnKUTe7/X2tSkP0meIP6bo+6pJQJ19x+QG7drkkNdgoy5dnghxghZata4RnIYozB1EwyXkay7/MNCEgMlhkOww87iHJn3oSgBuJHmSe8+2J0iRBHAKaesEI38KYxsIu4oAiIQDnsY7DxojAYweZuKkt6u0qSYt+plixhw4MPbHU9jX/H1JkAOil3c17z2Xf/RI55snS03wRinLCUl3ZZCrvo7x6p2pJujmfWjy5ghZtV4vs9O3bt/2wbzTIhZcCBxQBDn4whUvMbquwSMORnJi0rk8fkQrIWOwguTv1/qjw/HitstUauqC8nDi3zKbOHlJ1felsTCP64RxUioZGgjlqmEE4Vgzc3dketDru6s/J035IbjhQq4ksMHI7272nW0v2vzSOBhLeO97uBXE+wXAgcmbQ8RMeCwA0hYzCaMjfyW96aMgc9Vduv73JvfRc6M/kVJVwKvvrG3sGsiCv7sWmCR613jRiQ8XPN2CQD0HQDaQudjYaQgbSFC3U4+ZyB5xkPRVPqIGZhvA5jiABJAYBNvPjQx6UIBzAzXsG7P8TlfxxltvL1HOConlL96FFLhDEenN0+2BQQKRcdw5gyrrt13aqQW3AswncJZqMf9q5rZZMTvOHPB732klB/8lzqsI9AYOZGT9L0K+buvcvyGyg1TUevlYYJSs9pTxSjkTKMcExynEMW8JYePC1Oi5YH26FmpngoHIkBQji4GklJXn76nfUEQu9WzVa+fK+vTTCgMzToPmiEbt6n3DbdXp4xg08B3FAT54ugp66D/DkU3twXophh+c9OVnKBAxo196w6Bi50ov8V+GGI5RGsTiv7Oysfv7ljntdo+G9KmaoroLalaYGXRBbl3Tc2e9w21VYQLZzCZrY8qhQQrwxqVVH78j6z56yhE22FYV1OYQwj8mu9h3c73naABkU+2pzvKUzRf4/xQ09o0Lv9evm+Ra0edrX8Yk1+khlGeab1ZaqFfi+++QHWzcO4DT6D+SXVwKbEffh36KgvFp+5w4ua7k4BV519VtgaMeIwcYmNcO9naZGLqlqDc6s6Dh2vla/tdKDqOjr02XYKYT8u4Xd5qKFSK69z4LUly6GtzPNtA7kyUUePEZWw6kv//Fo/d5lIH/71zv/9rUbKKUA1x0slySc9u+PJPOLHHxg5xrGZceBvGvBgLsbi0DEagAUCEHMu8MlXkYcL6kkspIAFCi5+wx0EUz2oPXVampYQD7A8ToueqXvLVhST1/UFAIgE87DLKHGwm7QQhQ20j3QqfcGgP8qdU9wWJ4e75cPS4UnQieJJXje5ltgpP+IVgCUFqD2mdyUYQDcFyQXPv4WNmKZwUF1o5IBWaxf6Ix3HyPjpQSKRib6pqkhgsyV2LB1FQrGcd0ULGys0gdIOGW+qsCv4QIjU1T4sxZGLgf6TnWohvShDFNbJvy759MRBVWSz8DGd+brJ45wtjY9dil50j95brZGg1SirnH1ICvm2T2CNH4vd7BAfu8c5iI+WaQn6bGByi4+AQBJAZjFzx16NpPya8AlwH6/s1jBRQcT8MnlZQgO7uo7Ay0lK1ylemj0P+y99mbmdcCd1A8o1B3e9MYWStzKc17/o4X7gFhE884eg7I/eLOA0k8A4BuCemg8MafHpyn2ZP4WNakRsIXH30QOAoYfQeWHU43PUbwy4mneHPg0vGj5PCfd/GnNXXR8LL8s7dyqA4qTVGlsorhHFkvyycYSi/xrKlnvOld9eVnNOUV/jPvbDt5I7n2aFf1CS1azRbDeGtNPQhyWf2FJdZ/eOmjyg0cBiqPqSysXQLKMae4o3wNj0HazeV6EOSSfo9HhK3qHqn2zwbTdfnJrnpVDKtwJDV1lfbA3CENKMiZXRNylZ4VJKjIiWSwLoZb/FsfjHojLT1niSLO+4d8JM9CHbfKzAbJpwohGmfOUYDS/e5GPlYO6dSp8kklRknG4ZnVQ6T+8lkhj2SSY1lm1nD3J5OpdpZ2zDcDeey0QShZ+GE7F3LhsHWsZZpaXKipAJmpOyox5x4bnIvmFRquWSXYVdPRNras8jO/t5GKVbrRz0GQq3q2s/bUwYurmk7uLtZp3lY8udX0+4MgyDU9u/jotYNlq7QlEOAKCHJmRUJ+GJ4En4gVTjH+GDnQeaiMHH+khcLpSQkvy/USSI9hU4UmlYlOmF2+fA4kq6RPK/OK9tbn85HwdhjG/L7vVnt4qZmSmUX2XO45sa5IwYmfsvRFM3XtkhFF9QelPGIMn5GShxaGenpGaT6+vUiK+Lt8mVRE44+nNOxrYRjFnxWiIJLd8mK4B6P3hVx5I/DX/xPiUUQIattSWRX7fRbkqoleMAXkumofdm7nHedmOr0ZbnTbx+KaTxu7Db1gP7hfqHBVgzM5nUgvE1LXJna3GBPltamolJPsty1Uf2ommcYwwwseKfwNDSSlhVoegfylyxaRp1BH7+74nFS3SeqywlbPtobXSo42bStRE8Tf9XJi25W2kvTfBFuhhV6mIJI+Jzov9L9SlcINIoaGDl9jCVq/KMCRI9rRgThruwK13ATxsFA40HnomfZ/evTxWht6wstym8nhXmGQ+ZpyfbI1jYRnVFJbLGOjA+TqQHbyYT3UFv2A/Iw6egn4s2tOFUsaduQYtq3nDgfcAqnFr68+fN4/uROFfc9FUnKCex3mAWnI2s6fSLgpkRPJTVtkD6F7oZGgbb3SmYl+4iyw84iHWnQKH8OjLBr6HYQa47YFREP3p0iyOwYJ0IyuiccIDO12yuRgeBpDWVxvE14gfXK8G2jJO+6frfKFpAWzEn9BfupXp9Vmpqd+niGGVxQUlPJ7xTvkMHVmsiqbC4uz+rqh9CwXZGfItcOcAg9bsYdBtbhvA7dukIIK/d8DZ3quvVqckvkauTg6QTzOCNgzefMK+eFKybLf62Gr5QEEWLkvElR6DOJ5njkAgvz/TR3ttBPkQFFcZfmPN64SIrk557J5KQYtCgxU9WdN4lFjMC5Gzezt3VCwNh0M7OvrjRBzHYkBkXMl4fHOn/g+CNobV3hcLw+QwDBqnF3WGQaIkVeTJGXRarPTfX8LZS23h7GaG4PxWfrLw6zMRBQmyQFxoJd//yJXJl3FgUVvCHkJzkwmzVd+aR+8u9Fdgv5v3cV4K8pZIQ1sD7zAh7xhYAFZqpv/1DyOjf1ASEX8tyvKwMWfx3FfIfAfv0FBbl4fhl09rrJ8xcrV5Bq7Gr2XIuH1h1IDmDSQnpFP7AyePe99QE7YFkwNh8FiYcOwGgqQGaWBHTAhDe3jY6TIatexItJA9RM0MBXoJPo0VjPBPN0/FUaxntOmMTw7b6P7scly3WkGFXpDfjn0eSsAaRttaZfI+WIdhEiCNyFSwVxft8Dff3swa/nbnOyf+eVHkrQxzKHKAt0iGE5Pf4XcNOxjYmyI6K3iHFBUmjQq8RDNa31FpeVboxFv4sqBb2vzxw/+ut9KcLBrx7XnacrOXZu+q2+5zC5IJjvaB9aUF0dCHMxDCYqlaTgUEM52vEsaWbFypPm47i34yEcH1EKq4TDwmG2eUqsPbBk4728FAzsfAWAzR35lTgPpOX4KAk3wMgfQtilBLeZ/MVkZJPert481T9nvM7EgnjKAeoILo5JVGrkookJBE11WAQErtA8S3I8xOWzKK/zE2ulMuVP2FXsfKQfgHFDl88e4sGey28JNzlLI3GOUXN5GXcI6RCQkvsBJd2bG4r911ex9t//gH7q5nQa6Ll4pqNjg1AaHbnEFsn1/aSVvlFkkemiz5DtnaVXBDxe6OAyE3Gpq8dsCEF/LyYABGTPAYGBGxW08OABFW0/lDOzfwOH+tnbXt9yA8hBXWY5ZZ+1bzpVXtkLABr1oExSyr8UixEyhivlMrW6pJ3uKtmgCG/K+dpMyOY4M4uAG9hTlFClCEWZCQDsEzx9QlwiDgVkr3O369XA08a3cbu1Wt8JAzwF7iuqZYw0kqfsz40CkFQkLe3yqDyqC7UhJhKgoEOexl0AR7n7KDwlUQEaE4AwYGI2PVUFBUw/Sjx3Hl3k07zg4BiB+G5zlaIWuu+CpLAVPQaqTmT6727vW54ZxoAcCCaElms255Mf707NbOOJZUlV2PQ0YkfB0XyVNX1R//k5Z04TMBsxJayBHjnDBblcavCoApJxV/umVI9uYULUwHEzzHmkgDe51Lao4h1RyL1mnYkjzequvNLi9zJEC0s/+M42jARYBK8KVjk5JwAdavMNpKoYwFIjXc+LA2B//7fr/84UuAw0Ku17LetkU7xLTURikKV7YJ+07yEnTZXlJc5TCJuXJ5+RYd2WEqP1XMIXs+jDkUivBa1BuRYsVV+DtMWDIN2SQF+Xskdbl/crpGfvag9VQamZJstLVDQBgRAKm/9phUGhAQDn7jk2iY7DA7bqSbavTdHVyJAimVgyB7SOpPNPkKvHJOQpG/Sp2RCpzFuvnRDPWS5dLiGCQcyror6fBt5K1POotm7nYWSR57AN3D0qz3q3Lvbd8kOq0KKbrAI4AACAASURBVJFujtw6BxjGe0ImNdFkFFAHzSnfQtfROTQr67w9sV2qS6MRN/3zPTISxL4m8UiZ09bBOxqyDmVZzcu/YEWdHyHATU3/kydLEQmep1cvRz5A9rYf8rmV2DSowRLghWgmFH4pvfT6smdlwwpUeumIVCmXGI1x6elel1LmhRjc50hDJtDwrPzF8LVilEFXTdORDvcq10EYwHo1xcoL8RlPDvz0f//81097V39qWON3F8xl+TOPHj1Sgf/nv20dLCqcuBEcmC0H/rV3t24qyIO+l9x61Y0CoTAAJ7rppfMrUxgULXZpbDT2TV88XMJuEp/FLYsZXNY1y0LQDT1FH0dGU+DkiHEOF/ugnADYMe6eHhRM4gbUoHElz16bpUtCkyG19+odh1Y6kajvoroxVnI1sFrHDbOvL6olnyzgHf/YFmeCUbMf28sni8d5yflBn/Z3Q/Oz0BO8yt8XAvGWevkTvDFB+pPDAYxnTraMpeLbitNdmcWCRbHdrGyjL5wql9DpnE9GNyLNWp5qpUo6UkzqkqJdPcWIJsoRCEMremF/D5/fJeGwOgfoitigc9pEde6fmuAiD5jKcVGSyICr5wagTbU90PcyN5SjSQRhAQxacWvgAK29mqlUepk4uRVM9H5h/Tbw6ekZEHr50/NdL4idGvXyBUGWIEJwIJ4ciINeTl3skNsmXaa11pWXT/Sn0MvDf31CLw/PIwEhOCA4IDiwEDkAZV8f1CxEwgRNj4cDcY5jfzybEqsKDggOCA4IDggOPEUcELL8KfqyxVYFBwQHBAcEBxYlB4QsX5Rfq9iU4IDggOCA4MBTxAEhy5+iL1tsVXBAcEBwQHBgUXJAyPJF+bWKTQkOCA4IDggOPEUcELL8KfqyxVYFBwQHBAcEBxYlB4QsX5Rf6xO6KShoakX5zEB7zGp4T0Cfscd5RbcXKGUTZX8U6PJu7O8CRVp6ByzZa8GOEPR6n9MFxNPq9HNCIiYLDggOhOWAqPsWlkUCYN44MNPn7Rm0Xq07yHc3t4a0eSPXhZ1DDVGlzDjfgqV/6OzPqZu1HmJhSqJGupf+odMtIE97df2/bTZIQnd6G4fbuPqsEvh09yeTqTuIz3vPzfZ9scHlmOmrgm9EKTCHhWyDxASzFQpsIAvvxvv5ArdwRHAkJFpNEuOCA4ID0XNAyPLoeSZmxJMDeab10rF06z23ReFPu7LtBlIBf912QvqhgnqSWng1KgwU5fDp/eTtQ4w4L8h1f+87th9aiUiD5iVRtYrxVOUNI8+o7HTUmFVcr7rtspDHtHOJSoa6/4QXsgj2HnWQzU3YC9VM2KvA7A3Uf4UOb+qVXMnuWh02vYEGstAgh0x3N/qYZlwyqPkXbYrnqRycONk0WesVjUyeyi9/Npu2kuW0OelVfbMvQqBTZ+1nsBDfrMx6ZQUPQuR/1HNmZ6o1rP0bqaEZ33zMfobydm40sI3Uwu569kQqxD7Vnyhv7kbPAZOy7XILL6iyzsohBnVBSj7zxGPAouI31/ISFMUqscRGUTkrsh3e4b7+FL5PjFKiPCs5jwSn1hbVHUpA8HCKKa0SPw0Cr/Dn3mNVqN3qrsEqU3pQFc7b6LY7JUBp9AP3oFUMILUD060X9SN2LjdR4mmTlSTTE1vUSyzSCX6v643PCXQtq414g7Q8e7+hE1rE8wXgwuQAtDD/bV2vTNvvLlw9WWpJp5UsT9nZdj3zg5zyM/7LHxatV6d3+VGQRyFQEc9OnA6HgGYVzaxv1mSsiH7u3GhYV/8wWAInmGv1l3rUduO2RMyKSFuMT9PLwUafhZk9OQo2oGma5JXea6vqNWkrEgWiaEAdS5NIskvqFx6amiBJTuzFgj3L86kAg76um5Vu4uCzdxbom4Eqi1HxT7KL4f+tgyQWuOvKlTfyGQVlPH9iUABAFYZup3QVzQagvNQ+sbHs1ED7jFNzCmgvY3IndS6vq7h/tnlqM2NQmbg4HMrK9ih8iMlaiw1JSVOgN/t193AU+0rLyARjUxQTBOjC58C3tc+fyhm4+shFSQ38x2/yV//m8J+/ed+856mVLFf3OXpznKxXlOmxwKj6IrY3Y6fqO0rq7bR2lKn1sV009tieCCJjv+1YYjSXUtTGHukySn/S1L7ewVJXvsPQfzNSRNHCgV1dFc/3e719bTyCEH9MMTOew99jqbmqpw7PAUv9Vb6zjI2d+gLA6K30eufxEwKq8L3iHbl0WPJVPwfhhGBXN1xwXOgDAzjvtgfpnpAfhXQHJINkh1nz8tCkD5vPAjeSPMm9Z9sTpEgCOF60dYL3nfFNGCgTA7PigKfxzoPGWc0MO2nipLertKkmLSygAIglB8b+eOpTkt4hCXJA7Hrz3w93Zuz78tL7+zeYrWMryzM2bVt97sYEIbIsn+xoz9i2ZQBV81hfY6OjRDTsizVXn0Z8iiAHXzjYkOmFrmuQgmAkNzH5xpFHSsgYLCH5+zWDPxwvbrt0UpPq7iEIyrsgCWOYllRYk3g6CEHpYJanwXQQd2YtCCeae+5u9JRQyY1qMcl+e+O9016ftkXtMIEHAjwucG77pFQyNBDKVcMItInmd9BNdXrQ67urNxJM+Wl4nWTATyx3u5t9Z9uLNr80DsYS3vtujleMLigOTN4cItam3QVF6mIiJj0jnZCv2zr3b1CYT0cst2gry0nayiyy53LPiXVFFMGPN7JKKsg5VpaPnWod21qtGeEJMY5YLq6+6KovP0M+gogkcT3VHADZQOZiY6dqK0hQk07kskccJH0VyDZGysaU4UgAmXWw/TKPQU6DIKxj7OqS8dyMZMksn+jo9B2TX4O0xkg39WQAtGGreN0BgnfbJ5a/eHf/5HRk0W3T7YFBAqvo9HKgRB+XB8wncJZqMf9qzLYjxhYMB/zed1rJwX9ZMPQ8PYSU7n/06352u2OjY8RdmsEOMfdh8sszM/LJyPiYNKHLf411V0OcV3JOdv0tDZtxRHtnfnf5gxxAsuStc4QM7Cmi98k5RacmWWgZBsCStxwfZ9/AuWELTk+uv0wIA4aPUV+UeIotZ8kHPdFOZ1bXE0lkzEiVQrDJXnDFcDTopo919chfTbTkLmB4sLHXXTD8NGXn1aQ67cgGtRVlFYguOzDwEzdlO0iwrWpowg5bmHcgF49pGHBpOCLAT1sncfx4X8EMq8jjx7zDIVxUeazqM4kJ4MWqRAEYpQGtj+QBT8wd5DKlCfk7YF/LPZR1laXEUZMXTr0Goes71qxLtU9yrRj2tRvT040MoTZ8dGHwr/rvkB26rwAXavsR3P/Agehz3Hn0i+lp4uQ617Jn5Z+9jPmE2SQDs66F+VurgUDUm4Lk9ZMBbVy6gzA65a3LdAlmumuZt5uZ372X0oZReFca3DKdJkswU8RtHDnQeShjX3pH55ugrZte9no5SS/xrKm/BQID5o8FyOslKUT1mINvuCetqIj5/TKOmK7JDK7/8PrDD+EZI+NWWkS5yzDjrUVF+l/39K1nHpbgeGNlzqpdEiqUl9nJ9e3BetZawKxpcotT6jPag9elKSiYK/cNt1Vbcc2IwoZIorDl4gc51zIuPQymwHRccXdrGbNEWBoQYHT7w6BkI4Hg/Jzsq5vag0WRE2kke4GNoDHcnCRHSokU/yUFkTEuZICnId8Qsu7ReWmmg4qVnUUKUdwXwA8d+CVEaGwa1WhHWAh6P9JzrEU/2FYVlIbwwLF9yr9/MhFVWC39jOq+y5zyPEb7N7Ox67Gzz5K9HULfauB0MuOv6jkWDKfuw74OURT9QyA4394OZnn9FWphN0XN7KEhf38Se0qAaPxe7+BAoU7b1qMimG8G+WlqfIACwCcI8O7/lPwKyC3E/DTzWAEFx9PwCZLyndZ3v7jTRbPOAi1lq1xlR/wdXiez+b/sffZm5rXAHXSZglAvcXvT7jRReAYozXv+jheeAWETMyxPOZj7xZ0H0hSUzfwSiPPglTf+9OA8/Z+Dj2VNKg3FRx8EjgIa34Flh9N7u4S/nOfuvD2poez/3PGruadcImVJGIpSU1cRnxT+NjZKMreGAY/ja6TE6hoAQX5infw2feuhj9o3lH9Q8vBDSexZzVLGx1trUJBrsn/9h5c+qtxwtKtaxamAhvu0I/IcWX+9RyUSD0m+jvFqOdwvPA0QrDCwbZe6IwzOL4OAQSYyMRxxC/I9urdlARkFfS09Z4liLu4fomqrieiClKviCoOwwWXYIDUaZ86tjdIdHc9srDXSqcsBSyoxSjIOT9QPcBQAzR4v9It7qL+ZusnBKfAqmSDEie/sryk/0onBZfSIo51mHFmJpNRVx4SUIyJHru4ARBzLMrOGsaqMnaUd881AHjttaGHc/2hdaJFi7+EglUIFPHuqACzMuccG56J5hYZrEOSqYHbVdLSNLav8zO9t1L6Pz0GQn69FQQ6Xs/bUwYurmk7uLlZGpHHLf281/R4FuboEKT567WDZKm0JBEBBzqxIyA/DkxH9mlkuK17EmgMv7+q8uguQQhz786sb5hDHvmLl6oHz/smdW3+8SEpOgCk71pTGAt+mCkVGUmwpZeX5e9rBNRCRzjrm93232pPO0ZGSyQUKcO9m+8ATyUv9CGgwISl9az3N95stRQthHiZHKaFe8Nf/E+JRRAiKIoyFDhfzXJCrJnrBFJDrqkvYuZ13nFPpQnid3owH6LaPxQUmZe6Ywj8my3+k5ZXkAwTWsdEuSZDDc7DN7MRj0G4BSR9kvjkpBvS1S8YMCSEcR766P02S8Ihgx4qE1LWJ3S3GRHkJC/0XlHIIrNO53pX38hlCO44QNJCUFmpnI/lLly0ihl0oiBbvZ3fH56S6jdewPdsaXis52rStRFXN3/VyYtuVtpL03wQjKf+LY8Glic4L/a9UpXBvEUNDh6+xBK1fFODIEe3oAMeFrkAtN0E8LCAOuN785sL4M1X/9rHni11mvwJLwtFK5eLoj2R8nGTwv3vhZj7m91dl10BYMsZGB8jVgezkw3rILfqB+D1HQgOY8dsxvACpWBNxsnv8aI495tC90EjwdPNSve6orQSVxomzwFQFl4F4M7I2E++yaHJWEGqM2xYQDd2fIsluVinn0UT8xOiaKDvZyqkgpG/zeNA8wI1QiwXIuToTkUllfFa2+yVmL6qFQwtWJyBBNWY6nnOMSPVhZgY+Af99cjE7nVk7sXC5o2V66ucZYs4EOfONmg2YaZARt78H8twcNYXFWZDwNny6aph53XdMMjmoQ1gL1sNW7FHfLPabwK0bpKCCVx9AG07PtVeLUzJfIxdHJ4h8VrPn0uTNK+SHKyXLfq8Hq5YHEGDlPqf+tXhewBwo/affka/P+X7aZZZiHlaWk3RXBmkfv+z3EZfyW7CANztL0sIWdJsl3mimRUCD7JWHELm3NiypX2wSfeL7ICd7gHl6eYD8DKPGoYSwUuVRBJJXqWKKmMwvWgN1uUFQmQPHZ5QKRVB8m7J9F6WENHYdVL4HjdssSC3OCqIP2yr6D0vZDAf6U3/5qge1akPMvLYGQhLyEpyZTILgfmkfvLvRLWW+aVPwjvFWlLNCGtge0NWTx8ACspSf/vQ8jY39QEhF/PcrysDFn8fzu8LLOW7y6chPhLxsXDe8LCfOtDVXD5djmJVx+gIdQTVXbza3JDUdY/Ut39q96Go97lQc3nZw4d9FR4NUkQaDATe859ICBcIvs6AhpgKgtymZlDKlRokFbuD+qTCK9Zy2GUEN1DnhDzOZGqghCVuqFUPcBELNGd1ayjJntW0NHyNKtUH2LslVStoae7gke6hL70jRx6LTaD52JnMfvAmRCub6OgPF3vbfHsxa/raDHZr55UeStDHMoYqdsLju09NfITcNW5oYGyJ6qzgHFJUmjUr8DW667iEqLV83VzzOAwfG/vhOxj5ybIC1qP90vZeQHPPFw+Sk4aTU4tdXQwH2NL1JyBThxK3vTMejGhxvPd4V1QQsTsdcPRfPkDXlxRERDCH6EIZ2FcLQGARwGwENly/fykzlZ832KTwN463v8al6JLX6wBZyLQChKovhwmRlkNyv3jZkSel3l1gQTxlAPcGFUckqjUAUUSEs7WJ1gUotpaWZ5aSBC7NZSj/TYsidryaTzoDcqxQM9VV9U2B118WvWa2Ggh/avUwq8W/0tATxgDs0u8X0z/csZ5u/SHab2PzNQekoRslhTxcOBCISEl/gpDv3erE/uGr2vtt/8A/d3D4DXRevFFRscGqDQ7eYJCGIJ/9LK3mjjHfEaMD6O2dpVcEPF7o4DITcamrxy5DmAMTXYkxsY3EzGNhhcR9zDoyO/FWPs/O/PiXkd+Vr9eP0OQJZTjDqylw0YgDXuUZVxoCm+PHoGnKuPDnnveiEsRxhR0maPL77ViYXy2ZKOTsIuelqTjkka9V+tnpfy9YUFsLuPrW6pZ7sKWLywmEju0mZLQ2Qt1Y+Etn5xm5t5V1YGlJTSf0GLvN+vLXxTD5mCS6Ga6oPKoKBjIGoKBDnVb4oO3bHigUQBA41UHWCJyLkIIaPVUFBUw/Sj+ITCr/kOtWpmAgHj6g6Q6Y4zf9O1d4qYFBTRQ3ck8fAyY053PQQgB53A4AyFz5BpwfWMT9Qs8VTJ3kcwJtOzwGVpdPdXjXJG4wQ5B/iKVOlquxa1JtELUYkPN1XSdMX1Z+/U9Y0IbMBc9IayJEjXLDblQavCgApZ5V/euXINiZULQwH07xHGkiDm8lKh1RyL1mnYkjzeqshcZzNKYf0s/9M42iARcCKcKWjM0CXC7R4h9NUDGEoEK/nxIENO/euJX+9rp7GII696mtSc9yqvYqVjV3pLdZOMM06I3+VC2SGMkhql4xI6ddFJ3r2gaV3ST0lGjy+f/DUNG9nksHUKRSgKGcPfJo4hlN2/mHfeRkPtlOT8rzpHCwCA1Xh5EvCoG/uAvb/kovJOXLQrh5/eBowT91Vv0RCrqMQndPncPV6ZZsyKdBjJlVV/e2IVDCUJ5/b9iU1iSsjpCjnhjQC/2NsaKArrqy/tJfsX5I8oKyPjLKrYK/ALfzPiWaMvpYtvRjknAr662lwfGQtj5p4Mxc7iySPfeDuQV7q3brce8sHGommZlU5cuscEOnWA3XQDTXRZBQgs53y7cx0KIGG5FlghwyxH3uOVRHMaLe1Fkj5bGbBBJQ8rTSsp5L42qTCq447N0fAjD/7S/EIaIYEFheSBJnuhsw9KSLBE88zBEvGwryH7G0/5HMrsWkFDdcCvBDNbLh2vvQS1IGRO6ZUtwU6IlXKpS1jXHq61+V+tkF6NrjPkYZMoOFZhUVsmpwyRlw1TUc63KtcB2HktYO9UkK8+lbcxI0DELj+a0rt86ufUVaAPmnf6LyQyiv4fObRo0fq4//8t62DRYVbUDfoNr51gMkOX1DUCWJ0HPjX3t26EZAHfS+59aobBZLkkw5efjTzGaNosUtjQ6kGhwazteCVrSDHKPF7VrIZUrwGQin5OllL875C5tSro5ahalRMgnXc8kCgorC8kQkwWUJBDlPVt9KJxBKZ7QsjkQZW67hh9vXZLvFkv9zxj21x3gBq9mN7+WTxOC85P+jT/m5ofhZ6glf5+0Ig3kovf4L3JUh/gjgA0hqLhOukoLIBuYi68oifKBL+lqmvAc5C2N5nZRt94VSwQdk4PhndiCZreaqVKmkSPgYB97QuihGP+YhelGI22oUEc9iIRqf8kKwvF5zRT6Cp52CHgBSyYKA/14n816rX6aGjeJZwwgQ4Iqi2Bzpf5gbdJgRGROryj2LtpwuUNizPVCq9TJzcCiZ6v7B+P12/BNxun2y9nNY9lW3OizPlmvuyFsODUS9fDLsSexAcsOVAHPRy6mKH3DbpAgv80egs8MrMhf4p9PLw39Ai0MvRx7w1/E4FhOCA4IDgwOLiAJR9fVCzuLYkdjMXDkQSxz4X/GKu4IDggOCA4IDggOBAfDkgZHl8+SuwCw4IDggOCA4IDsSbA0KWx5vDAr/ggOCA4IDggOBAfDkgZHl8+SuwCw4IDggOCA4IDsSbA0KWx5vDAr/ggOCA4IDggOBAfDkgZHl8+SuwCw4IDggOCA4IDsSbA0KWx5vDAn/kHICCplbAMwPtMavhPdEfM1RW5NqOx3IvtgupL6Hvu67jC9RsGZpQ38fvJgQd1ud0QSUfpj3MnFCJyYIDi5gDou7bIv5yn7itzfR5ewatqe4Ost0/reFs38h1YedQQ1QpM863YOkfOvtzKtMZRV/HTUfUXPfSP+QnuWbFaHXrTPn33y+E9io/9x773qU1WMPuJsHBKqJvdg6lar9/UQPTIdM90rKspKaI2bUOAorfzfRVwXeqVIrFUrhBrumqYQY/gC1oYWS8ny+RC0cER0IiDyqeBAeeZg4IWf40f/sLce/QRMRERKHYuOe2KPxpV7bdsEVsUrKdEOjbHUpS+3ZHhYGiHD69n7wttSCTlijIdX8PDUahlYgk481LomK9WKliPFVYGWmkVj81UGw5EGRqy9tNH2xeWrfdlVfVdxbK5dKOpdjdxOo009l3DBrJR3JlQVOYxKSXwoJCtXamZRzIdZZv9rOhBS202CHT3Y2+bgOk+a+KAewJHZg42TRZ6y1+QqkXZM87B+xlec97ybWf6WmCpmT1bB8z/fvF+cw1W6PtziYvd5H16+a95ajUY03fKW5xMD3hhSxyN/qtmJRtpw1AT7dAlXVWijCoC1LymSceA8rFm2t5dRMVSoMWy2CAW2dFtsM73Nefwp9FFCmblZxHglNri+oO0SrrerUS26GyJGlSn1+FfaLOgiQ6Ik0HYwD0OZW2jOve3agdjFylZPDbO9PlcNSADrPAGWkii4/eQ7H0SGUtLBd4wUG3Y0ATiwHsfW6ixFN7QJLpmS8Wqz5+HH6v643PCXn3i9qIaaHl2fsNndAini8AFyYHfvq49Ld1vTJt0CfNquEpQNjL8qITwesnAAp7kfleXywdNqP/0vBMQ768/lDpaA4dTpe8RaAC/HplJHqcc5qxJmPFnOYv4MmDjT4LM3tyFFT3D51uIXml99qqei2bm0WBLjJQx9IkkuyS+sSEpiZIkhN7sVApK5mjD3jULjIghp0FFtKUrjYdnCYrXIzibkKDPQbdhMTk5OIKajPov00OWBxxdHPokchH8ngrumK3NwDHdkDqfV5Xcf9s89RmxiQzcXE4lJXtsejHE1saHg+2kqZAb/br7uEoVk/LyARjUxQTBOjC58C3tc+fyhm4+shFSYX+5fmrf3P4z9+8/7Ip6fay3HTKbAbHTtV3lNQv8GbbVkRe/qD2Wv2lHkZsr//w+nDGluzRHwmZd718Xf3DYP1svoMnZI654ZTa2CPdgdKfNLWvd7DUle9A1zVjjo4UTfRwYFdXxfP9Xm9fG48ixB9TzHqN8xNi+pRYnkv1/pmBn18sKZc6zqFQNNHCQ5P+vmUlkjU+OB3qHBx4CRrJKx7r/tuDI8Gp9mWbAYn+mhpoT8inE/VvzJ/BeDBIdpi1Pw9N+tAZAfxM8iT3nm1PkL0D7b1tneB954MVzJE/VaOexjsPGuOz44mT3q7Sppq0+GAXWC04MPbHU5+S9A5JkAOM681/P9yZse/LS+/v32A2Zb5k+egoWfD9+MbMiZy8OUJWrdfL7PSt27d9YMZRMfZ4OaAIcvCFT8uUoOsaPOJgJDcx2MaRWiXgC5aQ/P2awZ8aw+1k3swvPxLHWr35OgLDe7CtKqjuKU/2NSicwBeJxTXPHYPDDUhx6IgK9oMQAb83dzlSCgmIT1DHZwKdJO+AG1qzTzT3DjhQ6E7/fI+UFlLJqouNBxxJqWRoIJSrBiJwaE0eZu6OTA96fXf1BvMpP4RHXMiVzBLQodXd7DvbXrT5pXEwt/DedxOkYii2HJi8OURKY4tSYAvPgfSMdEK+buvcv0FhPh2xnDgvsryrvvwM+QgCjhbyZUvktcAk0bvGi058uJD38yTSBn/ZyVxs7DSEDSSoSSdy2SMOkr7KR7trR2pkjoqPSACJQbA9IcgKQgaPtbBimNKSZQjq5khUDxDUXw6iu9xdpynQ6OEmhbl15bnyJEeSk5suP0ji09+cPAgin1qzndvzftnfO7Ajj3w7nbdRNT/oJyeWv3h3/+R0ZB736fbAIJwt9N3ogXLV6y/jh6+PwGmsxfzL1RMhnmPIAb/3nVZy8F9iiFGgiowDpfsf/bqfBR0bHSPu0gx2iLmfQ345BGEl5yxJrr9MCPQRp/fwuOX4uIYe/coA89Y5Qgb2FNH75JyiU5MahHQno6IAH/Rob5nx97pwWFuosnUMniOgQcbGoFrCLkFIOCJTysrzv6vfYEK2jFr5sFqCGTffBYQjICfxR1kFPPTSCLJXumQ6cZxjsvKefjKopK+GewsPDDE6PughH9MzxplfMPw0ZefVpDrtSAIp5etNLqrTdF8z6ILcuqZsBwHldU7Z1ajlaxhwaTgiwE9bJ3H8eH9CXhlWkcePeYdDuKjyWNVnEROg0Iw5Y4mZO9w8K4qKs0BZzwMteR4u5/ZC0hnM26hasxPydyy/6e3pJtmFdgQkuVYM+9qNKruRZJpvhk4Q/lX/HbJDd9IC6e5r+zG7uBR42DtgWYSAx7PgnyZOrnMte1b+2QtHTJOLgVnXcssEgEDUm4Lk9ZMBPQSE0SlvXaZLMNNdy7zdzPzuvZQ2jMK70uCW6TRZgpkibuPIgc5DGfvSOzrfBG3d9JqDXg6O2560oqJbFz/IuZZx6WEQrdAga7N3t5a1VUvrgV/5ISqvIJmaV1qEzuGU+oz24HUpNh4lVuW+YQmD7BvGGPIbVOg20oXolFsgy9MjoEGmymoJQsISiV3SXfVL3tqwpJ4QiwDyOe0itbonWAx7PF9+qWerZMyHqMOTpHJ8r8JJ2IVMJ41DhEfjRWkYgBh7ybWPj5WtMicptB2RRnSPYQSN4ebLOlLQ4ft3WAAAIABJREFUxQuXFETG5zSj5RkDsz06Nw6GjxkvR8rmC0tBPf0lRGhsGuqvNPGJBx3pOdbCjxCimq9pYhuEgE0mogKqpZ+BjO9NXuaU56kqsrmNXY+dfQ7dC5Hn3DohR5X1pI16wzs7L9r76dBMolUgOp4nSOirycICRZw7Utylw20QJWJ7QTx/r3dwoNDMC85OxHwzyE8zqPh8igGRnSaeOuRGSn4FZCdifto8RxuwhMfkHiTlO63vfnGnqxjRBVrKVrnKjvg7vE4G+1/2Pnsz81rgDrpMQaiXuL1pd5ooPAOU5j1/xwvPgLCJGZanHMz94s4DaQrKZn4JxHnwyht/enCe/s/Bx7ImlYbiow8CRwGN78Cyw+m9XcJfznN33p7UUPZ/7vjV3FMukTIHWS7v5RxZLwsPGEgv8ayp93WMV0ca5jbeWoNSVktyW//hpY8qNxztqj7BxJrhUpfrQZBLog6F61Z5efphS0PkS7Aodff0VEFlYe2SMwaJHvkSlrtA7X8PG0zX5Se76qUjEUdLauoq7ll5QBpQkHN8u0pPPBJI5EQqKOfjE/9SB6NeqKXnLFFyxvqHINC67oKphEssrjCIClyMDVLTJ4OBfVuXzYUzkE5dTlpSiVEOIWgMLvRJZy2XvMUxQGeBgp6Bpq2EIuSgk5rC4m/7fBjmJrF3KtCZ6MgaPo0J66aMpSs5lmVmDXd/MplqZ2nHfDNY2mlBGw7TcxvmwV/IBVLB3UDjIuEolkK/Dt1hizk52eBcIK/QcA2CXBXMrpqOtrFllZ/5vY3agfRzEOTna1GQw+WsPXXw4qqmk7uLlRFp3PLfW02/R0GuLkGKj147WLZKWwIBUJAzKxLywzBYTZ2WSMWL+efAy7s6r+6CZSGO/fnVDfGMY99UwQpdK0ljwYExv++71R5eYqVkZpE9l3tOrCtiJ31GSh7KOis7LN3b0RD5Eka8uhH5DAFm6rdql4woxgOwRsRiF/QY1Hx8e5F0DLp8mVRE44+nNOxrYb4L3YknciJ1u47vIxi9Lyi+W/jb/QnxKAIggjgvSlpBrproBVPYBCrndt5xbqbTm+1O8lWbvYluDAzC3DGFf0yW/0ib4TRPSJMM73plXTefWzTve7OjEtVrYRqIUjgDTbdPTpAUpw4NAQP4c+5DSYkksbtlfKIcjd7g3iYH3JsdoBn3+V/VMtf1U0lC6lqYZUy1ZwBBKSfZb1tE/0mHDDk6j05ChpQWaon78q+NbFOxOo4w6y2s2+6Oz0l1G69he7Y1vFZytGlbiaqav+vlxLYrbSXpvwnmSJtfHG2bE50X+l+pStEG4A4xNHT4GkvQ+kUBjhzRjg5wXOgKRJ7RzmEWD/HngOvNby6MP1P1bx97vthl9iuwJP4k2K0wNjpArg5kJx/WA23RD2xbz4l2/Wvr58iXsMbBv0EdvQQyzmtOFUt2gsiXsNtFavHrqw+f90/uxCNLz0VSgpn9EV9IQ9Z2/lTETY6cSG7afD6AYXkkaKvzQV1x4iwwVcFlQkMtOnWN2QDWKSMkCBXCbct/oshMdtu5hBmcdreMpojHCAzMdsrwGMduPdU8iN0ann2jLkqtC6/m1m1XjkoARclIqnmuECvGyFcic6+MgR1i/O5GmsNWuNzRcg/HIUks6KJ5aFTtRtv7Ug2ev0vEWdNTP88QczaCUn6veIccps5MVWVzYXFWX/fI8OkqNsfaUJAOE+q4AjsMqoV8G7h1gxRU6P+zOtNz7dXilMzXyMXRCeJxRrC5yZtXyA9XSpb9Xg9bLQ8gwMp9kaDSYxDPj4sDpf/0O/L1Od9Pu8xSzB+zLEeerNYU3HixKPZLFO2tz+fyy2OwBDWzt3ePba1OBwP7+vrYcyMGRMaeKBXjxPdB0L04463+rznChlHCTBOm5TVAgvaRV0HXtLuwvmn87dt2FEiFS80c9hCBb3Ci22HCd2yyOJZudW1+6ba/n2g6rgkCJQcdXoHBvGapE9S4i1BDV1LzqNr9rck0bQjL5hDyEpy6TILgfmkfvLvRXWJiYGD8HeWskIYvLvACH+6OoQnE8jChUbIQ78bGfiCkIv6UiTJw8efx/K7wco6bfDryEyEvG9d9zLI8PSOfjBipiuXInJfoee8DcuJDO6vAnJeQ96tGG2RGaWCH+UhD+/gYKdKf9hVexopIBV/MP8EXS/RprGaCebp/KoxiPSfSMLg6b6PbXt7PaYWwk1m/AwtMffbsgM39RDMN6IPqNMhDyZIhbS0JdWVIG1Mj2gxYptvHIW9NGaa1X/qHAq/mquZYJc8NYuOsLuswRhK8CbEO5vq6BTaoTpO1/G1O9qPpImljmGOZBbrHPpye/gq5aaBiYmyI6K3iHFBUmjQq8RAwbH1FpeVboxFv4sWBsT++k7GPHBtgLeo/Xe8lJMd8xTnkpJkjjGB0vPV4lwyG0usqxMrxsxgA/sVsnma5hEbDipUjzWyinUQEmKzVQqqzXMK4m9TqA1sGzvtbwcAebcV7cxpIz3ElA9AcQNumkZp5HcFUY5A6r94+1mwjIZCkxIJ4/gWnflzbnCsbtqCACQVNNFFlDtiQpbS0cDlpygT2kwbEPWd9yADkgFbOfAu8iglsGG+vxB+A2VxJJwuTNoYV4jjBSSbIi7Z6PEtm2Ptkt4Wb3GImRskxqXESFMQ0JL7AE2kxfQEOu2r2vtt/8A/dHGmBrotXCio2OLXBoVtcEprvL63kjTKLRA9tlnznLK0q+OFCF4eBQAJbi98WgPhajIltLHIGAzss7mPOgdGRv+pxdv7Xp4T8rnytfpw+z48sX7FyNYioSbri5PHdtzLVEK3U6pZ6sqeISZiGnKvdpEwFMKU6qsFIl7AiEmLxMDleyfzGtSGgvfzMpgNqLF6kS4Sne/36Td/V+1ZutzMDmGOh54A9RVo+Ok0F9GfGgUhzAuY0Ct0+SPGOlERQSUGcV/nACPw4ril/I/hxNV9y5DTQjHMoR+pB+kHrJcs8mnccLNUQfZ3rpLXZIWW8shQ8BanOyLFHACkp4mp2vkH0chIR0sZIS0/kTI6q8HsExEYBIlVl12+H5stFgWWhgZY0fVH9+TtlTRMyYZiT1kCOHOGC3a40eFUASDmr/NMrR7aptpGwO0rzHmkgDW4mKx1Syb1knYohzeuthsRxNqcc0s/+M42jAZYBK8KVjs4AXTDQ4h1OUzGEpUEAzIEDG3buXUv+el09jUEce9XXpOa4VXsVexs79hT5TKHmu6KcPXiv9EnDcO5z8FyefE5OhVJGSFHODS45KmXnH/adL6L52ST/o54zrNKJ4daQvS0j593nKkJY5q0cSomyOlJCK59EQIPdEhIe/NeSyPSMfe3B6nSshzMgg4PjGUa0ucRuibC7YPDAf7Vtq9PKUtkh+R4y76F8nnxJ7OKT3eUq8cmqCYbnFfyntGG1gvixfE40Y710WRdEC3Mq6K+nwfmSBWXAo7zMXOwsCihranGBXqt3ylpA6obRDa+16Hbk1mGkdw+URjVUNJMnQhUzp3w7Mx1K0JdQ1aFXHnXB7bo4f77VmzJH+QRJj53fVMu2I8VT87fTjb4pPlNfAY/sE0zfhBRHBmsFRUPWoSyreTI6nJCgRAytys4hkGIaPE+qXo57gextP+RzK7FpBQ3XArwQzWy4dr70EtSBkY+11W2BjkiVcolZGJee7nW5n22Qng3uc6QhE2h4VnqPndmYHDZl0FXTdKTDvcp1EAZeO9grJcQrL8Vn/DgAgeu/ptQ+v/oZZQnok/aNUs9VGdM+n3n06JH69D//betgUeHEjeDAbDnwr727dVPhr3nfS2694kWB8E85ONFNL12UHIXRiTfDPBr7pi/9LUGZRFdx0+WKJeYiB+LLBkJK6xF1Gk2BC6mP5jdy2LndTs0nqqNq4Lo6It1gTLjU8xSQB0xSyBCAFslhzhywza+eM+m2wuFWJ+rq20tnGg404geGBnmO4cvS8dPsFyDi5eYbcMc/tsV5SdTsx/byyeJxXnJ+0Kf93dD8LPQEr/L3hUC8kOVP8Df4JJKuk+UgZqDwqlKKJIIN4R/0v2VaqHHhZfn++4Wq/1hZjaqG1p3OJbCIhJyCcUF8giy87Wp6rtc7bN3qW28VgLhCEnE4Qti2rbNig3pQsDqj0BODWVDkrJabp0mxl+W0YXmmUukFS7ZdrFJLts3TruZlGSHLw7NZyPLwPBIQseaATpbHGr3AJziwEDkQe1lOy742QG6bdIEF/mh0Fnhl5kL/FLI8/DdEZbm9vzw8EgEhOCA4IDggODDvHICyrw9q5n1VseCC5cD8xLEv2O0LwgQHBAcEBwQHBAeeeA4IWf7Ef4ViA4IDggOCA4IDTzkHhCx/yn8BxPYFBwQHBAcEB554DghZ/sR/hWIDggOCA4IDggNPOQeELH/KfwHE9gUHBAcEBwQHnngOCFn+xH+FYgOCA4IDggOCA085B4Qsf8p/ARbU9qF0iRU9MwPtYXquWM00jkORE+PgPI5EsReoYxO22YyBcujybuzvAiVWegcs2WvAwQ5AfZ5wDW9YcJN7qLRTNTRh8iKCoRD0m5/ThTzECvniEhxYzBwQ+eWL+dt90vY20+ftgRLfVld3kO9ubgVnOy5XS51DBVDEgEXC+RYs/UNnf05lCtjR8mTWlES2F+xSSkb6/CYVWK1Rh+70Ng63mVRGm+7+ZDJ1B/F577nZvi/WmOQ3ULw9ufdY1e3KqGYxaLEzPSFtzS9yzekZALtbx0xfFfxWKGXgsJhukJjszgoH5SEh4/18kV04IjgSrJvOWWET44IDC5QDQpYv0C/mqSULOn2Z1GbH0q333NuTTNkii2fTd4ZB7CS2nZB+qKCepHb2jAoDRTl8ej/hxHlBrvt737H9qozHBt7GhlJalVmqboaRJaE7N0ewb6kJQwz7Ugdo3xGVDHU44QVogboxBfq4bG6aPFvlMxP2KrD+JrHcldfSF+jPdardWfQg1s/9Q1BUP+9A0Qvf35kms+tXC9XaocuceiVX6g5S6hvjDXR6hSY9ZLq70ddteBstbw0I4jgwcbJpstY7x9Y1caRPoF5gHIhAljNtvtbUX+pRe2hGtJPJ45Ub9lyVQLFD2k6zDmD4WlqFb/wV0QoRAbFkENrVbfJyF1m/LiWi2fMJFF8+zOdOZrEWypu70c8zbREWpsp6QUo+sxCPAUuCY1ex8gQNBNVBYq+YQiNRh3e4rz+FF71KgfGs5DwSnFpbVHeIoo1AKZSkssdMfNLdTTtMupyhGpq30W13SoAGrAfuHWu8B2ZnU7CJ9snEct7qQJJcNdmJekpga9Dm1aLfjMw77LVKSgtLChKIg4CtfrPFgUxjdSzv6OpGJZ62abEuUx9LCmaDy+91vfE5di2rjXg2Lc/eb+iEFvF8AbgwOfDTx6W/reuVaYM+aVYNTwEijCyHRt3Z9eSjnutUBqNEXDJ68uGHkXfXTtnZdn0nUgLtU5tliqw/1mSssH456zfYuZV8ef3hOhkDNA9d8haBc8l6ZWTWqOM0MT58iBOxMUY72OizMLMnR7FS/9DpFpJXeq+tqteqn2YU2CIEdSxNIskuSeCFpiZIkhObcibkH/LkU+EBfV3VrqP6xiSS6diwUF7Ncl+Vz+jmBiledyFhun1oIJSrWhdwNqihKwrrKA2aDcCAlmBj2amB9hkne16RwaYCLcODLcPGScR00NtLLFrdAAbsPj4CajQ1qEi2+mYyG0u7CTXhh6Te53UV93VniImLw6GsbNNDUnik8wBR0hTozX7dbfYVWK2elpEJX77VWzH+RHLg29rnT+UMXH3kotRD//L81b85/Odv3n/ZdDe2sny8taZ+ALRYRZkGwXzyRnLte+uvn4i5FFxX/zBYb0riHAcvf1B7DcwJDMFyn+/RH6Fn+RyRx356OD6MnarvKKlXvpHYr//YMZqbPamNPVLaUC7eAxGe2tc7WOrKd6DrGuQory5HiiwaOLCrq16A+73evjZ+cog/pnBaNQrXXBZcksSFoB+X428peAGMfUsTy3NZ6wIhoIbeK94h4ZH8xM9BOKFZf3RQqfvA+Gx020Mv+UGjIstSptwDhVbNamUQeqJizeOJ5e7KZtYToeDSPuGEkZBvcsLQIPg7a9tAaNL37XLa+zwJ/P1n2xMkQwuQ3dYJ3ned4YHH+uQ9eRrvPGiMD9kTJ71dpU01afHBLrBacGDsj6c+JekdkiAHGNeb/364M2Pfl5fe37/BbIqdLB/z+74jmw4wUpCQoootpPzj1r3rqtPN0C28sUnwOK5ar5fZ6Vu3b/tg4REbAUVjo6PE6IaNYOLTAqIIctBWwYZML3RdgywEI3k0MVNzZ5gSrgWYJH+/FjsGx4vbLjuJNdXXMp13gDOVDzb2vmCtAcMiE820YTkaA6hCTLLf3njvtNeHz9KlHSbAA+2pA0id2172bbNyDk9CUybG/Klxkmd3PIItNwbhZMaZDQgBdwaI89NVf7OwlySlEoOxQSHf7HPm7sj0oNd3V39Wm/LTED/JiQBnCHez72x70eaXxsFgwx4vzHCKMY4DkzeHSCk3Ih7mgQPpGSBjv27r3L9BYT4dsVzZRpZPdrQPkC3b1/Nz0zPyyRlfx3j1E6QaXgtMEr1rvOjEh/zG5vY0dqp1bGs1yyvjyNxWoLO76svPkI8gdGtxXvB3mczFxk5D2ECCup0G/sgecZD0VSDbGClrgJzLABJAYhJs3zcIkfa8f9pRk6eTiwyp1CsPGnin75g8CtIaI91UdR9oM2kVr3PbG8wDpP82xJAXvwSJYbqo76T8cmZ93S31KVg5pOG7eDu5Fw4Z3QSI1LvbE8tfvAspZJFFt023BwZNkAA3bru0kxMSB4sSOM+1mP966MgXjxoH/N53WsnBf9EGxN08caB0/6Nf97NrjY2OEXdpBjvE3NvI8h9vXCVryvUO7HQXoDp3YwK0gPolb50jZFN7sD4d3eoDFK1tgBuzsHoL3muQT+Zz5bC7CJZgAvQIF0CXUlaev6d+QxEJF7VniUGhdLy1qOjwd/ITkiRLbpXIrQqkboTBTMPuCI1CoOxavW+4TbZw2PGBEOYt2VOUs4cuxYQiYkzAZ+ygYVGFuIX+aWVj9/ctc9rRLquP4EW2gwJZ1fTcWe9wW1WYQDY7JNTiTe20uZQkXFr18Tuy7qOnHOcH26qC+Klc/GOyajxT3uPndGiG9A3SnDfVXM++t7pPyN+RffMT4rmAqjwV2zaCX0JCxT84122D0TCdrLQwv8CcGHMzOxXk4OPX+RR0pDtKk0lnsBslOlzs6SrJtaLP176Miz3UTZYfqR8ByKOmCA2k/w7ZIX016ph01skuLoWvHv0v+ikq4DzeTJxcV3LwirygRQNyBua1g71dJoZuKeqNYilouHa+lv+1ksPo6GvTJZjpGG13p6lY4UD33mdBiktXg/vZBnpnsoQCLz7jy4HOQxn70jt+fTPdYpklFuOQjzl+zfSdM22NNA6e3Z60oqJbFz/IuZZx6WEQ7dgopXa3linyyRSBbhC81w9BRUZJCQoTf0W2BBWNGe3B65JwxdC2Sk1Gpm8989AFx44NS+oJL+a1tewxAJwkfUESS353fKxslcWwQqSGTjcCj8F6QjBy8AaVyo2UXRTnrTFCpO/Gjg+EyG9pCOFKk3SAohPBSyshZSDrpJxoINGwm7RE811oW3g8d2gMN1/ZkVIiaYFUThDe5Iuu5ZbnKg05YNPBaRNsEMV9Yam/KvBLiNDYNPpXfsQAONJzrEU/qApjOHDUbZ/y759MRPURyZb8HlSIqmcORj4h2WxWN9rY9dil575BuhfWym0CaKJkw74OUUiwk0P6+3aTM02ohd0UNbOHhvz9SZKpnLLRjGOk71inCQ3KkI+NRUAk4KW+4IEjBXsZCZ6Gij3bcxP1efk4CTICer2DA4XhJC7mmymBdexifJICkd0u1KdAUvIr4LvAAwQXr8BOn5d7KilBdnZR2RloKVvlKjvi7/A6mdX/svfZm5nXAndQPKNQd3vTGFkrA6Z5z9/xwj0gbGLmylMO5n5x54EkngFAt4R0UHjjTw/O019ffCxrUmkoPvogcBTQ+A4sO5xueozglxNP8eGAGsr+zx2/mnvKpXWtZbktXYzV+hxZL0s4mJFe4llTPysLfGrqKssVbZfAAD0Q5IqWjGLv0keVG452VWsBelSaUtlZuwRsAJzijseIMBiUGEANIZB6VRPDloQbX1yuB0EuiVs8ZGw1QNjxwQDMDaTs3LVpz8fjY6RIOhxAuMOqXWekew5wQT3g39lg1BS19JwlSs5Y/5CP5Fmo44nFFaYKJUhfdZzGmXMUoHS/u5GPlUM6dap8UokUns3NneMDuslJ1nIQhGaSlZXEsBDDBG3ZKT/SiUcBHQZHViIpdem1cEeudAoBBOBUruPN5rJg5s3dIJV1pXLQlkASaEzAMByz5KQ7jSTzu0RJ1y/IVWP7NTjHssysYaxswy+tAeAd5puBPHbyo9yTZCEAb8WFXNjLMQxBgK8VDnMpVMDr+MmcvTgscXhAwzWrBLtqOtrGllV+5vc2qt8H+RwEuapnO2tPHby4qunk7mKd5m1F3a2m36Mg1/Ts4qPXDpat0pZAgCsgyJkVCflheBLOUlY4xfhj4MDLuzqv7oJ1IY79+dUNs4xjt6Z7lQu1cHptqlin3MLn7EURg0R/a7cEBuit9vASKyUzi+y53HNiHZc7J8tOND7XLhnRFPewGCjAvhZmm+ZiWE+2yfNnpORhdAn6Jkgsh9aVbHurWQllgHCHjIo2S9iF8oJ10MJfXrASK3++TQWJCdmMMIApINdV26xzO+84N9PpTRASdNvH4orWxo7KuutA8uBXuLhOshr1WjMK4RSCEftO+k6HAaXXV/flai0RscIsVT002YuFXzizQaKD2gDQ4KH+WTCjLoqxhNS1id0txmR9BgUo5RDcZxE/iL88eCrKVi0EaKTBNHcFg/yLJ1tl5llH7+74nFS3SeqyQpBnW8NrJUebtpWoqvm7Xk5su9JWkv6bYMhDNT3sNdF5of+VKv77QAwNHb7GErR+UYAjR7SjA3HWdgVqwyIWAI+JA643v7kw/kzVv33s+WKX2a/AEku6qFS+Znw9ces7Qqx1aOOE+I6MjQ6QqwPZyYf1y2zRD8jPqKOXgHe55lSxpB+HxYAAWdv544IF8nDD29Zzx4tw4NG+L9pb35zd3LMTCgCMd5/PKumJFsHjhQ/dC40ETzcv1euOGlVQaZw4oeqI9cWbkXm4LJqcFTSGcfFgoftTJNmt/tHnX0bzxOh5KDvtbexUkEO4Vv9QNEswsKqFQwtWJyC9NGY6nnOMSPVhZgY+GQ6ZR7SxCKmw5PkQs8xsoPZ7u5KuiYXLHS3TUz/PEJ4AhT45+05nyUdlfX8P5No5agqLsyDpbvh0FZulbXAWYPadh8/rU1aI42fg1g1SUKH/i+JMz7VXi1MyXyMXRyeIfFazJ3Dy5hXyw5WSZb/Xg1XLAwiwcp9T/1o8L2AOlP7T78jX53w/7TJLMbeW5WTFytXkM/Mk7PyVC+pXgIkgi+x7AJmXn81uLWoMka0z71DUx+G//GFRuv/W69uV/7XzTsbsFpTCrDTZA1j0f4sRcRgVyi43GoPUyKthyojSamto5Z6/Sy/pZ7VyQWpxVhD9x3z8toYLS9kMB/pTf/mqBzXaMAnWaMHO28i7vTFjjYbHa0jN72S1WP9SZ9MOHrOJ+UdqCXkJzm3GPjHkl/bBuxvdJQ79AnJlHmm4nBXS8NUHdBl9aOogS40o4j8yNvYDIRXxX0eUgYs/j+d3hZdz3OTTkZ8IMSkXYyPLpQhwFAxSTJlE9OXL58jqfWWp87sF69UwR87OItrz3gfkhG2hunAYCAK0a35oa1oWwJvU4tdXH77YVbNyNK1s6wKgJwoSpgIQYFXKTzATzBAzZciP4mfN6cnMsDwnhBFMRut0BGBhQIyOf92EJFcpaWvs4ZLsoS69I8U0qDt1h+cfLtKkfCLVhAdPPPqnTYF1K+nN+3JovRLioIM2f7QOhCTBmxAtYa6vm+PCzLqs5W9zsn/mlx9J0sYwBzsLdHMcTk9/hdw04JgYGyJ6qzgHFJUmjUo8hNpaX1Fp+dZoxJt4cWDsj+9k7CPHBliL+k/XewnJMV/RrucpFlQh5y52sTN7Lp4h23YtoEIxqIlehWg7lkgMZzsuk71i5Ujzcd1biEsfHVDrpIbDQAP6jEuQnuOnJvlVmSfqiWCe43OrbVPFjxFwn328/0ZGsd6Ep4IsyBtMFAbJ/ertsC0+IWYqjkoz9cIWRiUnNH6ieAgFTfRIBQQswD5IcD/G5LApr+LxCcooFFlT233S0xJkY+/QvN3TP9+zWDgBHBKYlH8Bf1zQNobSHPoW+qM89ivZbeEmt6BMsjFou6ZgEBWR+AIn3S1mx37YVbP33f6Df+jmMAe6Ll4pqNjg1AaHbt3SHiCe/C+t5I0yi0QPFpDeO0urCn640MVhIORWU4tfBjUHIL6WkwEDMmaAwcCMitvYc2B05K96pJ3/9Skhvytfqx+nz3ayHKq8nfhy02dvbVFkIWRV1X625SQXzm2KdT4HU6tb6iHlWiWSprftJmVyqBrEwQ1AQnYRI3choL38zKYDagxaGAwQ0Fd9YAsgqb+s7Qvyuf2ZGgYIwj/XqC4B+XUfj64h58qTc97jTkLa/NnegeNj4LxfOkNMHt99K1PeJoMPIuCukpUlKczQwr+F+G2CMgYikkCcV/n8j6e0NKieUANV90c/Iu6BwfZYFTQa8SD9KD6XeVhbN2reuU5amx1EY2UpeApSnREhtgYKTQ7wXALLNj0oSMcF+BfqpXjqpFhC8E/T8m2VpdPdXrWRORghyD+El2d4RqEp4J46KCSHZxFf2COXNd3z/Uaqyq5FvUnrY1TE47tKmr6o/vydsqZJzSmMAAAgAElEQVQJmQTMSWsgR45wwW5XGrwqAKScVf7plSPbmFC1MNSneY80kAb3uhZVnEMquZesUzGkeb3VVxrcXuZIAeln/5nG0QCLgBXhSkenJOADLd7hNBVDGArE6zlxYMPOvWvJX6+rXx/EsVd9TWqOW7VXsbGxUzowTRkyv7XiJFoYtlKNpDz5nFQCRe51BvOKcm58KdVs5xqUwfgeeMs7p9kqKDKAmjMW0RIEo8ohg1whUoc/PWNfe7AaqtksSZaq2VACYIThsz0GAJRLuCer1g2mVgziKTrRs6+oiKawwxNs8A+emubtchMaZRcI+FbOZ/ihm45DdnzA99KVsvMP+87LC2FZHtb9ocCsWLnFs3CcIApVdp9QAxyir2XjLQYYp4L+ehpcJ1nL7aaZvjNzsbOAeewDdw9Ks96lyr23fEA3vNZg25Fb54BItx6og25VkwT0XaeMbWY6hBqw7hpv9nWDx0F/6fzN8HpYrV2KqWKdpsEElDyal0XxeSqJr00qeurAnqpu/Sr8Mw2pA9O6nGkmhX+jg5/GkbGxdfy8CJ8mmocgy9wZIbQZGPXNQ1lW82R0ZAs2m1dTEGUUUlSEx8B5sxXiMgbZ237I51Zi06AGS4AXoplQ+KX00uvLnpUPbFDppSNSpVyiGOPS070upcwLMbjPkYZMoOFZZYdsmpwyRlw1TUc63KtcB2EE69UUq2/ETVw5AIHrv6bUPr/6GWUV6JP2jc4LqbyCz2cePXqkPv7Pf9s6WFQ4cbOQOdBVXxSoibI17fzt5197d+sWg7/FVi06JPmkg5cfzaQI/lnHdhpWijVKNbawCYMZXtkKchRp1vXCQEUOpeTrLPM07yvErGF2awhVg4W+es56C3oc+n5r7HuZAMMSXOq5yVsJB5WRmMfFRSMy+KVvh6vTF9GWGRTKrYJEOhUpo9F9Gk9Ohq9bR5711qJbOQLoHf/YFgHUXEBQsx/byyeLzwXfgpmb9nezzexYMFuIOyF/XwhLhNPL406FWCBGHEDtfxQ09czL5MCHT4yBHeQBFgnXSUGFJeiv3a48SJ/45/hvmRZKGA9q9pSVbfSFU6EFZeP4ZHTj7KzlqVZqnGn42GzzrR1rl0UeEOC0qK5KyJQfS7rygejKpmhsmpS+FQz05zpl/nOiFHVx24K4+O28OqS4Xynq2W5ZoUuroKeMzOJTTkuTnAJOFoFMHt0mBGfYFq9l5y3Ee1p7NVOp9DJxciuY6P3C+r0Qv6p5okno5fPE6Lgvo1jyZX9H3Neb5QJGvXyWiMQ0wYEnhwNx0Mupix1y26TLtNa68vKJ/hR6efivT+jl4Xn0BEHQOrVPEL2CVMEBwYE5cADKvj6omcN8MXWRccA+jn2RbVZsR3BAcEBwQHBAcGARckDI8kX4pYotCQ4IDggOCA48VRwQsvyp+rrFZgUHBAcEBwQHFiEHhCxfhF+q2JLggOCA4IDgwFPFASHLn6qvW2xWcEBwQHBAcGARckDI8kX4pYotCQ4IDggOCA48VRwQsvyp+roX+GahoKkVhTMD7TGrnw210qyWmZfxKPYCdWyir3wOXd6N/V2gQIpahj3iXUJlHq01S8SzwgFC3daJcDDa+xD0m5/ThTyMwy7mRJOYLDgQaw6Ium+x5qjAN3sOzPR5ewatp3cHLeuJWk/Sv5Hrws6hfidiwBLffKXY/qGzP6du1vp3cTXU9EQQEtlesAErGenzv+rRtwYxYlRHQnd6G4fbTNrFTnd/Mpm6g/i899xs3xd1ouEGi5aPTJ9uXmpVxtUwgx2g9degUbrMqKmJ/iSpwFxi8j1f+4xT4xU7y3DvmOmrgt8KpdYsrQ/PtW01zOAHKA+h41I/X2QXjgiOhMjr6/E4xZPgwILjgJDlMfpKulqPO6t3psYI21OMRinNzbMAS7fec1sU3ZTFMz/D6gnwY11Y6NsdSlJbcUeFgWIePr2fKFKKDhTkuqEx6H5VdJmXI8V6sVLFeKpuhpElIex9Ys4Qq+1BrXUQwCaCPOGFLJK3MQX6uGxumjxb5TMT9jqk0LyOqsSdtI0K/xIqvDIHF/6d9CQRX7MUbC3YPCZ0v7exT170peeSSILZHKsxqLXO9l9JrtQdpKzmwTg0sR2Bj+nuRh/TEEyeEC1vbdaJ+auJk02TtV7RyCTmjF2sCCOQ5UpxUGDBmvpLUTbtYPukYV8vS2knraJ2SIsRv7nmYxpOW0o0sMjuoMNp0eHvCOCsjmyCgLLiAMqbu1YvrcdNyrbL7UOgyjorAxgUBSn5zBOPARXKm2t5WYXqILHERlE5K7Id3uG+/hReh1bKg2cl55Hg1Fql4VgESqEklT1mxeppDflpM4GKamjeRrfdKQHKkh+4B61iQFDbgGFHeWI8Scjathu7OdhdExeH4UjhKZebzXMnjIIXSfMUsawkb4c2ynfYudxEiadNVpIORGPtiHLhOYH7va43Pifk3S9qI0ZDy7P3GzqhRTxfAC5MDvz0celv63pl2qBPmlXDU4AII8uh1Xd2Pfmo5zqVwSiYl4yelFt5RrT3lJ1t13ciJDT8bg47Y03GirAwUQFAr9KH20HW+l5njhEo4Iu2EGYkKpx64NTqnmBqJLvTTxTPZhwYbPRZmNmTzcAtxvqHTreQvNJ7bVW9Vt0wLWbOYdixNIkkuyTRG5qaIElO7MWSkH/Ik0+FB7RoU7vI6PubSaZjw+J5Nct9VT5jFIHU+GS6fWgglKtaF3A2qKErCusoDZoNwICWYOvSqQEbQ3do0gcd5ZuKyMXJ6QLNm4C9wEfA3J3iNOLkRqYCnXAOUCeiip93QD1hJLkIOO81uwg3NXYPUufyuor7Z5unNjNGHfmcYXZIit3ic8BU0hTozX7dPRwFirSMTPjyo5ggQBc+B76tff5UzsDVRy5KKvQvz1/9m8N//ub9l01Jt5Xl46019QPQq0NRpkEwn7yRXPveeqk3uSnC2Q7OYznx9R+e3Ham9rx/cufWJ6af2GzZ+uTNMzd7Uht7pJtBuYj9SVP7egdLXfkOdF1btDqNFGVkcGBXV/tk3+/19rXx00L8MYXTqqW+4Ay8JIkLy1MSy/G3FLwAAYPXPLE8l7UuEAJq6L3iHbkUjeQnfk42cTOYpbcD+/vA+Gzhtp8Z+GQY1FY8JVQQTRDiCQlFslOPTf+MOj1EJKjCsv/2IJj91UdCnK8+13ZxKp+RrwoKOGEk5EfoSsc5YCcYJDvMmpfDcQTdGfCNJHmSe8+2J0hOAWBsWyceR2xsEgoxT9Cnp/HOg8b40Dtx0ttV2lSTFh/sAqsFB8b+eOpTkt4hCXKAcb3574c7M/Z9een9/RvMptjJ8jG/7zuy6cA6dl5RxRZS/nHr3nXV6ezwE3a/YuVqcu0Jo1mQGxkHFEEOcghsyPRC1zXIQjCSm5hbI8M6KyglXAsmS/5+zeAPx4vbLjuJpVNkcf3Bxt4XbJu9TjT33N3oKUFjAEGVFOLONt477fXhs3RphwnwQHvqANLMbQ940MUgiV5Hipv4zrYXbS68c7YxaH7SUtArn0A86PTqsQYN3Xkb+e6rBanFX/UgWj0TklKJwdig4DX7nLk7Mj3o9d3VG8yn/DTETxLY0OPV3Ux38dI4GGx477sZVjHGcGDy5hApZZ7F7bxwID0DZOzXbZ37NyjMpyOWa9vI8smO9gGyZft6fm56Rj454+sYf6LjvH68cZWs2jWvSvnYqdaxrdUsM40jPKefwif4uwwSa/Y2dhrCBhLURHGUPeIg6atAtjFSNqZsRgJITILt+zi9lhLpqMnjzOkc5ZIbO9HR6Tsmj4O0xki3uguSmo6aPbaK18lOg9sewMAAoPoCAJlze2GgqucY+Cz08pKjQH2gjvZp4u0l0smDMfurMOh62JF909tzluhJSix/8S6kkEUW3UbXgp3q9HLgxm2XdnLCZeEXgMB5rsX814MhTNzyHPB732klB/+FHxRP88CB0v2Pft3PrjM2OkbcpRnsEHNvk1+OAs/owE53AaqBGxOEQLRacs6S5PrLhIBbnd7D45bj4wz6CG7Be205N/IlZEiK6oMe22XB6197rf7SCdbeAPFruBflxxQDB4O7trggMoDHQ2nLrr+lwfMjCvcQJ8MNmyU0TIvvDgRG3QXDT1N2Xk2q0263oOmirALRZQcGpuymbAcJtlUNTdhhC/MOBN4xDQMuDUcE+GnrJI4f7yuYYRV5/Jh3OISLKo9VfRYxAWQaFGUwAkPOm4n92YYqFI0OstxDWVdZSmwFv4QHBJ7PkLw+k1ihjwiTguBgzuD3EeTlU8t2MWgS6N2ASVP+r54z34sjxVOTGGrpYTgpEZbkWjEMSWvSg+2/1I8gL8QA9t8hO3S/BrhZ4GpxKXwL0efZM7hjeDtxcp1r2bPyz17GfMKswcCsa2H+hGggEPWmIHn9ZEAbl+4gjE556zJdgpnuWuZlQ/2791LaMArvSoNbptNkCf2S4jk+HOg8lLEvvaPzTSuLuLVePj5uboV2pq2RSAUPd09aUdGtix/kXMu49DCIai7Gyu1uLWuLwgKP4WkfQvonRqjpeRDZEjRAL6M9eF3SelEcVu4b5mgY2FOUs0fBDhEAPawgB8n61ijE9/WkyhCAoegUF7FPl8DQAWkiPla28kso2EnRiS83fXa5RIsQVHahQhB+JH3rmYcluP3GypxVuyg3JE4m17cH61lVXsOwOO/QGG6+M0dKSTl9IwWR8QlR6FpugZB1Twk/eTqoWNnZcYjivrDUXxX4JURobBrVaEdYCHo/gpqo7mqrCkojeODYPuXfP5mI6qOWfkZ132VOeRqj/ZvZ2HXI5ce+QbqXMN5cuhCv0cK+DlEc/UP0KJBgxI+CU9sUNbOHhvz9SUzgfQLmj2kXZQ7Y6i9A2BreH6uy187B0X7PfSiXNMtxWxPNt12HciVDt4YV7xAbeATeroEkvXu9MidlEMgI6PUODhTqtG0eATxhvhnkp6nGfAWAT1IgstuF+hRISn4F5DdifhoXr6BMnbdPkJTvtL77xZ2uYlwy0FK2ylV2xN/hdTIU/GXvszczrwXuoMsUhHqJ25t2p4nCM0Bp3vN3vPAMCJuYYXnKwdwv7jyQpqBs5pdAnAevvPGnB+fp/xx8LGtSaSg++iBwFND4Diw7nN7bJfzlPHfn7UkNZf/njl/NPeUSKday3JbUa4FJsi6Fgpwj6zXRmF7iWVM/Kwt8auoqyxVtl8AAPRDkmsxb/+Gljyo3HO2qZjRvSBjT0uFQ2L+1SZ0yFhglqz1liiAHKsCP8F179//P3tfHRHVti+/+YvJS7Wu4DaZiqAwMX4L8wUc71Zd5PDMho4YE3sN+xZSUVi/0vlFR341GTd78AcbG2FpLrtja0mdjctXSB7kEnUsIj5C20gokF1E+Bey8zjUSSxql//jS31r7fO19zj5nZmAGsJ6Txjl777XX3nsNnbXXN6jE5RuQ4gPIICTk+u1xgDTuGS4lH6YNt7iNI+F6BoCRq0uk7zz2ftvW0oPMnSDc/MdyHH9nZQYZxf6bGN1s/1CA5NU2C1gXhFwVlxl+6HEZ1kmN+plza8tshuFwwDaMMWmJHiMX4fDMo0FDurPWAPOjFxTdXYTlxICcIYK21Ewnxs7hVUCHISkrAWRlfdaXpFzdBUjFJE3HW4tkOJcc8pEOkhGEuakoc2bbJiWBeIr2wIUD/fVANDfTQ3w346lx1Uq3NAUJfiatzswaxsw2Vpp2NMMDP3awE3Xv9OYXAi+85lw4zil03AOtA1zmUugXqqOn4EQ6fDFrouIaGLnKmJ1V7S3jq8s/6fTVa9/Hp8DIL1cjI4fHUX3uaOuGhrP7ipUeqd/039sNf0RGri5Bik/eOLptg7YEAiAjZ1Yk5PvhaVjLFKk9sPgUeGFvx/W9sCz4sT9bVDdPP3bzbW9wSowcIF4t28zAWbFkBiy6V6sl0EGvyMvz1JTMLLL/as+ZzWKGisx+RGOTKBbvpBtCAf2SvLUiTUCkSxxqYo6pTdEdBBj5PtLEqQR0EBZN/pgkZVtp/v62yXHi5k9ngeExHGL9t+GX9yPiVX6+kZdIaVWsj1WQqxp3YQrwddUk7KjhDecimV6EG832sXhAnctdU/hmsvwjra1EHeKOJA9+iV3grsUyOYEgrk1U3+AWgh77Dtqhw4Dc68uHs4QGfFuSQuLi6CeI4jj/SN8XvYFpx5FT08xMklTWog8maroTqrfAFf+eqRm24bATz4lvWrDiytRNCd1NxmB9ZjMglIPCQGf+V8aZI8g+d6ikKSnU7mfyHx7e28Cff5Fl9O72T0llCy9he3fVvew52bDLo4rm7/g4tu1MW0/6R0GCMPzhKKdmP6c6mvtfrFB/pukQYqhrD9R78MeNApw4oV0d4LrQNVHN4rDflxMFnG983Tz5VMV/fuj9bK/oT2CF6WYpV75hHJ66/S0h5jK0cUJ8e8bHBsj1gezk4/pldug7mDZlk/7G0zVuKdwOJPXSC4QUHRoO3gTGiSr0Ng0cl8iqiYCh9ryLSWMIrxLQ8MznzUz6nw+uZT8n9CA0ErTMGAqZxomjQCiCy6fj1cj8kbOo/jgI+b0tk3eGHs6QZJcsjPIYomsxch5yMjZzKrJtHhll5GDs7x/i+yNuUf6K0KzzIJuqNumZpBEpPwyGnIVIcvFaHSlgD2jIB8ZWe2TyVP3w5xWyqlywCYl/9w91klyFQSbmGyVsbuYzv+MU+NyYrpFQuCapaXbmxzki/iLk6Dv9VYOq7ilvLizOgqA73REMCezwFF4+rk+3kXg0J27fIgVl+l8UR3qutVickvkyaR2bIvJdzXpn06PXyPfXPKv/qAerlDsQYP0hh37Ybi9jCpT8y+/JV5cCP+wVhZib83KCgVufjN0hhL/b4Unz1zuW04GBB0cpClMPvjHpDMC5Sy9oKvf5HgxM8p1gsz8A94A3/WWMzn++CJ+4eVPfBUFy4pTA+t9ipEkYEUqWEYXUQ15FXpIzkQkhoJNmKEMt9+I9ek4/r5UhyisrSBO55DqECDCVzfBEf+pPX/agRGsIsEbbtupzAGlHWEqiTP+MlrMWm3SNAlMVvX4LcFHLeiYKquJuCVkL9zaBE9xPbYP3t7uk6Dt+IcZiUsoyafjqJ3QRfajqIKv46YvTGh//HgP34/7YaeDiTuJFXuCFHBf5eOQHQl4wLmzhx47CK7nQqXPYvnr1ks66bES6mD0YIxf9gzZy+ek5iflwNHO70q994hIjoOu2fsAkj0jQzl10qVToCW+NwDCK+oCiNP3t3QD2W+nATGH6B9iJwafdi9Jk/B4pB2oY17MYL4/ueCYMOIqVkI1ZuvEnOksg5I8ycsWQQfPSy2s4aha+B9Pt4kVt3SqNl6Pyw1pMp5p5sVAeHCV5ijLAdEVuAJPVrEnltAJzP90hieEudhySmDXS018U4JoaHyIvZhslJxWUStIZDrVt+YJCvOWDALdAyrefZUqB8T+9/dSzb384wW7vh5tKPle2V3q34OXAlmp2kUutXeysntYLZNdexSmMHVmid/S2uw7edvzyk+dPc9vmRwkNnScZmam6fnFTvATpOX1uWjQhZc8HhzZeaAwTm0dNFfz0sVHuFEjqjaXFTwgvx8An4Nwv3TVESfFEAkNyQRjBWj8hqja1whaKWUhYRMgeQkGBHKnMBOusFJZmGpOmQMbkE4RRKPcyrVx9pNtSQvFu7aYy++MD05VQKaIE0YF/oq5pOk04AEub+SEK4a07k10mZnKTaVKyGu3UFAy8IhKe47i7yezYdzurDrzTf/SDbg7zRFfrtYKyrQ6tc+g2F4QW+Ot58vo2NHVH8jhKKgq+b+7iMBByu6GpU54tBiCBprMc99CvxWDQD9ntmFJgbORvenwd//MxIb8v3aTvp20rXk6k8Ko31ZBxjMz+ZMdZ1ddaiHGxO1Mrm/xkv1vdJA1v20e2Ma5qui1dPbh1/3WiyOLUlPDheVXsBtt5tn+ATlG4dWrlkR2gQmcDviGIvDPTLAWsBL9Pw0nQ+eBSvcr70dd9bCO5VJqc865252CXoKQuOtRktoTuSI99k2YKAx4DHknAzisCnUuTWhqcwCEHqu5HPyLigsL2VAUkE/Xi/pF9rvayorYseUuis5fGf6c6IkJsDhSaHuCpBA5fQDrmP9SZ10oiOGjFK/pmqtzlJbPdPjXAGpQQxNSGzSpFjiRTVzgl7h+a0Tx4UTNGgUeDYSGwUlZ2vRyPioGlezwNn1V++va2hil5CxiTVkdOnOCc3a7V+VQACDkr//OLJ3Yxrmphdp/mO1FH6lxMVDqEkvvIZhVDms9XCYHjbEw5hJ/9JY3bAywCWoRr7R0Sg59o8g2nqRjC7MAeXhAFtu45sIn87aZ6GwM/9oqvSNVps/IqFvZyug+MhIbQZzk4G+qkPVK5i+L1XZp8CQKvkcErPcSdc0vqIViOBRin/Eh4ePO27HcmQUgAarU0BaHlEqA/uPDI6V+hbBJd2BTzuYr8W3UUF2JD1CDJ/BVSvlX1noOz0HIsx7OTEUzaFwTBD2fsyE7OkZrgva+GtNHIeHR5007d5UdPOgIYjsuUgVtRzyG3e+sKP/RTJ7sPvFWNNVoMOvYCTk9rco7sP8ScAgd/089UI3pfyy7Q6GCcCvLr5yNgsl0T9blFJnYWSR7b4N5BaNabVLlx04bkMqZ4uiXl1iaBp1sPlPo05COTUSgO3tCcmw3pQroRZrIx0G20OBBdDBUADqu5S+EygZlq+Mh7xCWFhNG4LNr0lpNAi5T0NAlrqrpobxz/wTItEP2P5m/tidZ8rs3Uv1GXdUjLKg5GR7JgsXl+dcUrwrs0cjkeAaK3OyGeW/FNK6i7McEz0cy6G5dLrkAeGPnCVtky0R6pUC7RCP3S031O19N1UttgPsc9ZMIenpbGsTIbE8OmdDqrGk60uzY4j0LHy0d7pYB4ZdD+jB8FwHH955TqZ4ueUpaAOmlfQxYmk+epX3/9VR36v/+9pb7bL4tKAUyVc/vIE+Ax9x+9+3SEhd/ivrUuvdhEgST+pIOXmzovOdqLP+tWYWzU902ciFTgG8Wti17iWK+FjbnSAEBEDqXk6zTzNO4rpAEJ35QbgDqo8zJT+01e9PXWWDB5A4YluNBzwSjgoAxS0c2zONV31jNO7VRe8IuTc9kCYbV0quwXanrtUJBE+Wm8ORm+bt03IvoTinLRSMF3/3NLpKDzhEPJfvwAHyw+T1TLa1raP8w3smN5nSOeu/nHQsAeTi6P5wZs3DYF4Mcdk4TruKBCFzmJutLET/w5ZsOU2bEI3rOyjbZwyrdAcOSD0Y3I9M5TDERSioDHo149hQGK9DVp02rNRyzcJIdpFfCZTgjWb+YrmijYaOi5FFodnOjPdQjpb8Gt4cLxHePIpqDlP+/SCDfuriB/of1DF39MVdMAMLO0DHpMZ7Svcsg4zbrPh2/J3wjyeHTOiC5LbrTbiDM8LVieqWR6mTq7E1T0nbb2O85UX87obbl86b8dKUGstA+wYvSoVoyl31rsd2CUy2O/ho3RpsAyo0Ac5HJqYofYNukBDfzJ6DTwyszl/mnL5eG/IVsuD0+jRYEwzSK3KKvbi9gUsCnwGFIA0r7+UvUY7tvecpwoYO3HHqdFbbQ2BWwK2BSwKWBTwKZAzChg8/KYkdJGZFPApoBNAZsCNgWWhAI2L18SstuL2hSwKWBTwKaATYGYUcDm5TEjpY3IpoBNAZsCNgVsCiwJBWxeviRktxe1KWBTwKaATQGbAjGjgM3LY0ZKG5FNAZsCNgVsCtgUWBIK2Lx8SchuLyqkACQ0FfZD59xAW8zyZ0OuNLNlFqXf/CyhCErARQIjHQPy6miFVaI9GSRUUdO2RzsXU/p0tolqzEC9cz6HfHjUkZ/XBBfkAmIKzJgA2d02BR5zCth53x7zL/A3tf25Pl/PoPmJuoN8dXNzSIsROY3oAvJ3IgZM8c2XYNHnMqPJxcz3YXKWe4GK4UQ+yyyb91TFl8fDqP3sCxZiH5n9vHEVVxKehbB4hyKhZJa0zuTXEDYJKzODpldbZ/qNzDT1nDJ+XwW5TihC853pLAa/8po011cBfxVK/jhMphvkaqsrgCafWMQWhib7+SS7cEVIWhl5fj0T5Ha3TYHlQgGbly+Xb8Leh0QBMZfC1K0PXCZJN4XczoyegL+2htC63Ylq4tWoMFDMw58fJhw7L8h1fQcFRlUeL05HqmWMp+KmnpckpXir/v55fe9zusTvzM0Dt0oKhenr+SND6TnkYaSj75ShTIsoFzo3G8uNy4uCDiPYUhHkhpVG3qaVsIb+FPKouMipoyw7ydd3ERL3RlG3FHKts7XVk8t1FyllP4JPKGILRXrIbHd9oNswLP5jM4AtScfU2Ybpal/xkqxtL/oYUsCclys1yphDMcXBmN7YvErLqRXSYoOUsOlRGZRsnTSm+7fxGh9KLgptVj6XRe5Hv5IgbbtcHQSyrLM8gEFdkJLPtHgMKHGObuKZDYqDxBQbRUW51HBffwrPaJX04FnJeSQ4s8lde2wlgpsIhQmFa5Ka/s5sjcIm0ym6XssmlhklxMCr6GZItgtrMVg8tNx4g1RbDL+UvO1e/lAwF1ANksLoRdukFFfJ8MRa9UTiSnEWm4tmCCuXC4R4WmRFp/+IBm2cYTt9ztc/xapl1REvRNOz9xsqoUU83wZcnhT44cOSf63tlfcGddLMCp4ChDkvx2qnaW534JWeC3tSERfyxeScuCYM35ixTt51jD4wPaoHSpAd3yDXYKV4gdtpVVljtNIyQxNzSi7a+QbrAyZq9mhqZvcPfd5E8koetFT0mhY3i/mRklYlkmSnVKckNDNFEh1YUhNrludT5gF1XdUqMqb1zfQVWeZ+ugNVQqLcK5YZhTKgbtI6PVug2QKwkvcIKKtTHJb4pHLjtbGpBzoz0LYyv5TA9YjKx7a6xXYAACAASURBVMrCHey3bCxxpoAt7FOqXF5b9vBi48xrjFJnqnU4lJXtFVaUWdiKsZntaZjozX7FNRwFtrSMTFA2RTHBBl3+FPim+tlzOQPXf3XSrUL98vyifzr+31//4QXh1s15uQEc+OIw2ZHtP3zaI3N3A8gCOuDqEPQvYH40Uzf7h/1j2R+eP7C5Mj2aeWFhx8/52z1+6eoTFnh+AOGXWExKzu8MlrMMoiSFpjp2y3nMIMrQWJ80ta93sMSZn2SofcnAxvQV9OpqneyHvb6+Fh57iL+mhFV009lz90cSMnerUiyPUdyaG/gIje5oQSgjGhvD+81s3pFw5eAIGpjztqsHEa8h6lU0EMpYyCertUdJIXAa4XnR6BCE70iZQyTeH/l5qXpgt6gWLVxosAAuHCTRm9x7sW2lpNWHFVs68EJjYhpQd/J4vXjr7/1SH58tT531dZU0VKXFB7uN1YQC43869zFJb5cYOcA43/iv4x0Zh7648ofDW0VTouDlMD1957H327bub+zZ855bhO2x6Ut3ZpDrgfbJytjy3fGxMRLnsoOLsMRj8y0KN6owcmAPYMqlD5quwcwMSnKBulWIJDadirsWYJPs/ZrCH64Xd51igzGnc0YROWvNWxqroztjTOBGBjnVSA0EktAJCm0SuNjmfq3w3sX6oPiepDusZGCuH3Jqu9VBqM3Z+xB3oO2NaiCkQTwvW5p2buAbdZb1S2IqGRoI5TLc3Roe7jqzg77Afb0z4EwnOFg050oMG2q8uhopHdZOgsKGt75b47dHyfToECmx6bDYFEjPAEnzq5aOw1sV4tMe021Ex8sJScnMIuRC59X33FtMcT6pA13+0gvkffCrit+zCEvEb/PhMcPvMlmIjp26sAEHFYieauXsUxUBWtk61xF+P1FDSI5p8/AbpztnlpP8zlBVDiZ/ToKEg7yV3AsSNkKXFOo8yADPxEuaJh9AHDWFExU9p8DioOd2zHLaKxiYHyTB/+ObUh1yp9WXkqdNjNlbQunz9yGULjLvNuoWYFTRg7B+V3cXAboRuM81if88Yrb73x6iTt/b58nRf//tHWzZn6jk8K8/H2Z3OT42TlwlGWwX8x4tLyfpGeAzNDY6SbZQIzqiYr3kWOe1STRUf0sXU6zsPe8mV3+CPZob3dWDOcAC6WPuksagYudK0/Bfsz1oENzb+MQYKfJugyMwE3dRm7rmLld0aLhFU8Jr/YCJPSYhzBHIfnfOfrqUcmRmXWYtHQYZyPyYkSzBwJhS0vQU8t7we0kHxwj/AN2SKR7mVDF+FbMciFfuW+2wWgoV6TNV7tpmS/VsQW5twzMXfcMtFWEc2ayWIoSyXlXs5sLPkrIeoqUc5+vdv3lv8GRVeQagmvOd5GQHxl29KI8YpQf84/JInkcg1s8llHk9mqCM4JITHLwMfjfjKQijOQcD8/3tLnDIV7xt5AUVnwM46YTex14CsfrXIm2AcFqic11foG217poiAqXxZiWFeiG+/x7ZrburUf0/yS4uga8e7S/6KSLs8e6bOrvZc/SavIpJAXIG5uWjvV0CRbfk9UaxFNTduFzN/lkRIrvR0WHhEsx09La716B6zncfeBq4uPTUuZ6uM1tCBrE/4kyBjmMZh9Lbf34DpHXhEz0vB+00GVNxUd6Q0Ra8KYnpwE5WlCssMLWyJ1h8unzr5dIrPTtT6BT3meBZUj55gOGRW967+eg9CP8Exg8Ck+CR2A8w2p7NOIrN8vMGLmuyBwE+2bl91xcXkCiydXka9nmLcuX6jCuPgil00dvjYFagGCibBD53gR4TgbVjEiIfgcBNpXG94iqoW9mKUBTU+piRLBGWklankF0db7cezLlBKQCbwi3tO7+N+bJ0h4p1E5XhYpxJKZ5SOoIcbphUcU7maHNF+dWrs2/MBhUtO4sUnctWAU/6KUSob5reyivDjqAsq3tUZgwXjtqamc7D0wkoPmrhZ8Dje5PVO4fK7MU6dh1yrgkc/ctn3mp+vq8CItCEz/AM4YhAgVYmcIxcZmBvNbsS0O2851SFpXQemu6DK0IBmfqOXZF1AmD7w7/fb+091QFfAdABL/4hiDg3kBSxKCpEFSNEBPT6BgcKw3FcNAdAfJrhgsIHKRDZ7OKl3nwp+WXwJ4SGfKN5Qt3AIrxQTgm8s4vyzommbRuc2050tvsczNp/PfD0aOaNiXvInpGpu3xpDK+VAdN8l+/54B0QNjBz5SlHcz+79wtdAgF0S0gXhdf//Mtl+n8ONrc1qHsoPvnLxElAEziy+ni68BrBL2e34kMB1ZX939p/FlvKpXWj5uXcdifPV/mBifolRg5DW9678n751pNdlWco3wWd/LbS/P1j4ImbIk/s6iR7/RKD5FClpm7g2koDlxiQJGali5DrGpeFS0C4PeC8T97MofoACYfKlTWU+HbVD4xcunagA/xOZRQ125oiAc6yp+XsreRq5pgKpNln2E2GPaYZZmO/GSUjOsUlskW+MwHidI93oz/2XgXclvF3Nsj1RNJo6rmocrL+oQDJMxHHxSHO4AzFOKkxVl55aWR793VRWJK4zJmQEz1GLhLJ5iOBAf0BNXjD5cYTmh4IsXlOKIdep9eu67DS+w14unklPLI7PZ5CciBn7hnyzJnOj0ihFC+nw2XVlNUhrAwtLY2Tkt3K9zL3kyBADkEQ+JuHsyQxAVvKk7Q6M2u4+6PpVCtNO8abAT92KJMEn/TmFwKDRXMuLHQKXf8guA4ucymUwevuFkaaCFDGpgsV16wQ7KxqbxlfXf5Jp69eu5B+CoxclbMd1eeOtm5oOLuvWCd5m23odsMfkZFrcnbxyRtHt23QlkCAa8DImRUJ+X54GrREZjjt/iWgwAt7O67vhXXBj/3ZorqY+LEbTzHeGfi2yMszZjSo77/ac2az7BxH+UHj6Rq35GV29SopAyk84ocucahJvhngNI7LguwYwR5glv42INrAJ8TzSNYfcMNXr14Clbh6X6Fj7gP+/Mg94cNuMuwxuQ3NqxHZKV4tY0hNzK4F89qAeBIwreZceQh+eT8iXuXnm/7Kgx8yZy0WICnIVQO9YArwdZWvOGp4w7lIphcgJGghjsUThY7darmk1VxoWeje6EhC8W6DMKqgQLqBNT0rm4rjSq/0KVGb3p9UHYPiD6i/mlA7Aj9daine6bQF/M9b2DY9RSDOjV4ykHTJ5Q1resH3rVC1d5iSFLzSaiWNC7fUytRNCd1NxmB9BgiEcpL9lsDQgDAMEbzSLQGVNCVMjh35D0/e8yLL6N3tn5LKFklcVo7k3VX3sudkwy6PKpq/4+PYtjNtPekfBV0hiulhn6mO5v4XKxQRSgJHDHXtgXoPar8owIkT2tWBOKq7JqrDIrYBlogCzje+bp58quI/P/R+tlf0J7Ai2n2hpVl5xscGyPWB7OTjSofyuUN5gc/U4leKjl/unN6DbLKnlXjOMINhX3GJrBr+usBNimgP3AzTxq4tQuf86dERsrFUH/hOPeEZ9YApVhwIu8mwx7REH8lgDE4RyTILggk9CI0ELXOOzk31E0eByiEEq5kqcgE2i2qgg5Dz3DLDSejhDEl2SX7gghUi72LkPLxGoFu1Q54N4uzdiBGtTE1+MBkikokXY6PB5Y3TpUuYZLM9sKXaI5On6oc/rzAPUAZOD5ckyI5Ocg1JYOR9aSZ82kGvViRphGQ25JHWe6k1zB2rVHpfmb87m0gqhNA0b3EHFAnPCfYsr2X8oDlzZmd+nCPiLwJ99Ip3y27qzHSVNxcWZ/V1j+iIYEiBh3TwslmDGFTxe524fYsUlOl/1RzpudZicUrmy6R1bIp4HRFsbXr0Gvn+mmf1H/WwlXIHAqw/FAkqPQa7vVQUKPmX35OvLgV+2CsKMY+elwNzLTqEXmPSwzuIKb3sJ1Wzt3WP76xMBwX7Fj87Fpv38HtYyDp3bl0nRCA6RIkzvpsMu5kYnSLsOgsAYPKGKlj0v8XYH0aEkriUgoD/RG5HXuLVuTwEtDCNedYaTuVrgFmcDmCffWtdwGsTCp8ZlRTOwCM7wG1bIJRPNYLntuIxAElDWDqAIA7Wd1XDgU16goJcRiqzPJOcfMZ53zeB6vqXHkCJFMElIClF7FMG2d2NkXWWCxJMvEMIpocT1Gj5ifro6bz8KD7GYlLKMmn46vVee+jcQFZZ7yI+o+Pj32Pof9wfOw1c3Em8yAu8kOMiH4/8QMgLxoX/n7HLsqenFXzOs1KlKyX1abcEp4OoZsdgboIKdlaFG34qdZsfmQS9ktkT4R7MpkfQv259kQCKesKn6a/WAkDsCrtJBLA8pgniyLtjcIrIF5sXJOQNNcwDhtTsreX/866NoJiYAVPEHVKmFEbojHhmbAGB04C9QOaXECa+bjjQNgNJYEKY/UawlKNGFfoFowvrwuQzpCpPW7cg9bkvo6iiNvvjA7JuVZTXI+pRKBbKg6MqZSI8GL1MpHJ0w4R6ieEudhGijxIsPf1FwYyp8SHyYjavFefAqCSd4eD6TBsoxFs+CHALpHz7WaYUGP/T2089+/aHcHnWnh9uGvRd6mB0vHz8XOMnJP/9GlkXrTJpFR2+TJ4/3cV1kNTKIzsGLneeBwU7b3XmwUQt8RKk5/S5aQlcDGDcgwh5ZH0pe/a++q2/6SoHPd3eNrCxtNiKlzN7CLtJMQBzTG5xtcEsofaZvMz3FCboYt5Nk6Jkv/XS3VONM9bIEwrCCNbW08OMUitsoZiFhJkKEiSwh1BQIEcqM0EDHIAA91MVfSZ5aiXAB72HwR+ec1OHMPHEpj60EDO5SBW0ok9UaUhrQZKcINE1RTPM+jATKsn2cpbplfnbn+n2DU2ZzeH6ocoLKS4T6BI4qCgayS5uM2Fnopdc3nbd/QxM+NGp/cMuEzGAs+rAO/1HP+jmJkx0tV4rKNvq0DqHbt/WGuBP/tfz5PVtJoEeLCB9d5RUFHzf3MVhIOR2Q1OnDCoGIIGmsxz30CNmMOiH7HZMKTA28jc9vo7/+ZiQ35du0vfTduS8HAKxciDsGEK5tFxpqZVNfoio3nF6UkEOoWX7yDaD8L1lC7DDwHrlEqBAR/BJ7wH73X6GlULoV2em6qQW8R4iWMwEZLO/bcel0vLzinoAY9L2k0NN6h7keSD7wpVFumRMn953O1OlQ9hNhj1m2CVM9q51R3oKbcYivtGf+90pCeCRBOy8IhB1levY7HWmE5K/wjaixwZi9KmKQbLbi/vHquGrvZp1HAwD4DsNcjNqgEHNUF4CloJUh+kqsyG9mzpcAvoGwd4PLDnCkuSsSuNIMnWFUzQc0Iz8kTOhGmhSkFteAs594b8pvKWZ6BIi38VCIKWs7HqLAHpFLN3jafis8tO3tzVMyVvAmLQ6cuIE5+x2rc6nAkBEWfmfXzyxK1KjCCFpvhN1pM61uUll5xBK7iObVQxpPl/ltTqXj7lSQPjZX9K4PcD+QItwrb1DYvATTb7hNBXD0tHvSVh5654Dm8jfbqpfH/ixV3xFqk6blVdZYUoUJbHJt0ryEzCTDwe13CnSRPQqd2Kpkv1S28wqvNmzqyhNs7IzqzIZTgiR8PCZWCBsejgDy7ook9jwMOyz2IMUtA0w30JMGo9WxqYcE5ty3JoeP4zgHrC0jOzlB3lgRB7vKXs+OHTZvXWFH2ZglhVWCWGxSYCGJ+wxKZTVEhFR0uwUCh1Kky/JPv9KD3wpt9jKNHQfsf1nqrEP6o7IKlx0ME4F+fXzEbDmrIl6IRRAhy1mmacqA36pN6la4GGGJKczxdMtKbc2CTzdekLEmI9MngRuZQ75lcvYCn2zmBQ1mavuqrjf16Iwir5d9IDKcjKe+HzwkQW6NeAU5STQgkFupiclBG5pEPofQ6FctwvJZR3SsoqD0dEbH4vN6zcgeUV4Oa27HnNc2xC93Qnx3IpvGqR5meCZaCYkfim58srqp+WKKZDppT1SoVzaOfqlp/ucSpoXYjCf4x4yYQ9PKydlw+SUPuKsajjR7trgPAo9mK+mWB2xX+JKAXBc/zml+tmip5RVoE7a14ZkDMogeerXX39VG//3v5AuxX5sCsSRAv/Ru0+HXfXw0vVDE3+IjUZ0CY4p6a1OpL7WFmFs1PdNnMpU4BulosUXjOMyzxemDwGnUykPDnFYjA1LlixhYL3YJAQ0qEzGZRxVI7KMq6k9olnqIJAdNfyQwp0JEaSjAirNhuYSkuCSQR/uyHg00tj7U5mYy8pTuA/pVsR1Rdww3icMXze3PcyAO49suxHvhwPc/c8tXDv2DZTsxw/wweKxX2UJMKb9w9ASrPp4LfmPhbBfc7n88TqMvdvHkwIy2zCxT+vCovCI+HPMFu2I8thZ2UZbON4AUHbkg9GNiLPW8M5TDITQf1tfvZSBt3yl+wH5nPqiN8vh0dwMKTZaK6vKDcoNC24NV4HvLDzRpCqrcwOtWqw/s4CungrRGDkAyUeWmCgwckwOL/I2Z/Bxr1oGPa47ugaqLmh9VbhMqPoPioLZ3iDQJ0LPg+hWXyxomns1U8n0MnV2J6joO23t92KRfxmuY8vly/BL+S1vySiX/5ZPa5/NpgClQBzkcmpih9g26RHmWlcGH+tPWy4P//XZcnl4GtkQNgVsCtgUWI4UgLSvv1Qtx43Ze1oaCkTux740+7NXtSlgU8CmgE0BmwI2BawpYPNya/rYozYFbArYFLApYFNguVPA5uXL/Ruy92dTwKaATQGbAjYFrClg83Jr+tijNgVsCtgUsClgU2C5U8Dm5cv9G7L3Z1PApoBNAZsCNgWsKWDzcmv62KM2BWwK2BSwKWBTYLlTwObly/0bepL2BwlNzY47N9AWs/zZU/0xQ2W2Xct+67NAohWTmiWQJyfCZOzc8pA7JXzWdG6GvgEYoqiKBrMh3c3FNnGNmanG6FCR0ELL4sFmaIZ8/anstk2B3xIF7Lxvv6Vv83E/y1yfr8eigFh3MAZJN+W8sAvI34kYMMU3X26kf+jij6mvafW7wqQjtTxLsOUwU26c/VYh23zjqigzj6783TrSXR+AjPceotski9r6fRaqov2OrRZjCZ6w9plQfU/nWq++ognOAlS9xCR9ugBr0lxfBfxVKMlupfy1Flnt9CiwiC30Tfan5LPpBeGKkLQyQQ9st20KPK4UWHpePn7u/PjOSrYMyeNKS3vfsaBAnjBfOqZufcBVHGHWktkz02PxCvhrayC/+vRAKFEtyB0VBooc6pURjp0X5Lq+C5w6rPJ4cTpSzM/6Dc0YT8VNE16SYF6rLbl8fplHSwopW831/th7qmIWy6bp7iIWJKNDloXdRJNF6XIluCS2FLpoqqEPcq1DlTn1SS6PfPNQxBaK9MAFoj7AFASTUYn/2NR1lvRl6mzDdLXPLmSypF/C47S4FS/n6m6xhxIWHGMBInyXK3G92rbTegKtMXpdhqFVvKavdpEtm1Osp9mjjxsFVj6XRe5Hv2lB2na5vghkWWd5AIO6ICWfafEYMKH36CaufDitrUJMsVFUjrLsJN9wX38KL4wq6cGzkvNIcGaTu/YYrUSyREJhQqmrdi3UiRkOtK1mtAgMLaJ4Rd3DTBVPqCimhylxFg0mM1isXC64uNAiK4nCW6MZpsXs7/Q5X/+UkHc+q454VZqevd9QCS3i+Tbg8qTADx+W/Gttr7w3qJNmVvAUICx4eU/rBSzcidXKkemOye9YoXxynLjTF372zf5HPWlut1qgVYgRqpVXky9uPtosj8INY8WbBKqOblF6hNPszseUAoNYQ1P4RFN1u3/o8yaSV/KgpaLXrBqmcI0FdSatSiTJTkmRqxU+wZrl+ZR5gJb7NUXNCzZ7R4G+ECe3OiiTvzTRtFM4TcTnpkXQoKXHJ9Yqxc2UGYiwidZ0UXq4z5GeU01ch9xo6rlITNn5VOMQeYm01Af5mTKqpCp3bTNBT4hSS1LwkyNvSZXLa8seXmyceY3RZ0y1Doeysr3KdxE5wkWC9DRM9Ga/4rIq3avfSVpGJiib9L12+7GmwDfVz57LGbj+q5OeAuqX5xf90/H//voPLwhPZc7LuzrJF5SR6+alVjaV+tsnCfL4+D9XD1bf8F/pYdi2XOd77A4hj59cPn7O3+7xLw7p4v/lxGUFsdqT6tgjXQ9NqlifNLWvd7DEmZ8kle0S2m4jRRkZHOjVVZ70sNfX18JPC/HXFOBkimSsyO4qvC/QDRb93Q8vVgR07oAtFTJfREa4+15n25xHM9Kr88O8gB7CQahFn/EbQJG9VDhRpKgQAepMFXIh+Q6uXhnAYEFVZs/5zDvUOx9oW8n3iFbS+mBvg2S3qKxqaDqA5gz4RhK9yeCLt1JaFK4sLbgl3t1BQ/iYvnnr7/1SH5+9T531dZU0VKXFB7uN1YQC43869zFJb5cYOcA43/iv4x0Zh7648ofDW0VTzHn5Zv8Z0QToS9/p32MyFOvu6dERsmGLnmen76zZdTDWSy0KvvGxMWLXJYwrqRVGDrZwRcZE0zWwkFPwUxe9nXgBm1XctQCFZO/XFP7AR+86NR5GZXd5JRiaeE52DUt8rZn549cjgQkpHoX7hpGqSd8pfSV4ZLEecNlTmNwCTipP1UwV4ZQK7FpcBXSSmEqGBkK5qisDCyl6n7s/MjvoC9zXK8xnOsHBojlX8kiAa4qrMXCxzf3a2klQ2PDWdxFWu4+hwPToEClh2vbrolAgPSOdkK9aOg5vVYhPe0zXNuflplMWe+DGxDTRm8bdZ97TtmH0njP2aNBL+NblL71A3gfHK/sRUwB+l8lCdOxULgQWJahELrMZ4DEVAaI6RYu3Mf9e3ACJgbP9PHZgIlVT4RvQlWQXJ6/KN6qyC3JVtX/Ei0Lo4MqEpIjBLQHlKwinHnj+PoTeRebdNts2MUjAM04nl4Owftep3ZxwB/AHQOA+1yT+87Dc45M92Ol7+zw5+u9PNhGW5PQlh3/9+TC78vjYOHGVZLBdzHsseLnswkaxCt3iwMTuPv6tvOqrbUG/0Wt9/NyObP8ABVGM9NhI2Vaav9+/1U2u9OxkBBQZlWTIv0QI4z3H+9MpaHFRcjAHWCl9tD2EBVCXUiBpB3tM5vjUL49okEWHhlsq4XLFehHud+fspzjA5C8+lLrkE/lipmPv7FvtsCIIMi3ww6pt1puBuUkFubUNz1z0DbdUhHFk42YZGsqlIdeBQwq/pGBJWQ+nSCLtD6r6cDpC+GayqjyTRkX/zkz1JzqEZt0Q+OHzQVai+ZTVJeeVBAfJqlRyl3XdF4HLffR0hnHeXi7+mgyTjNoCmCj70lNgajJfSfpnpoDdytMTnev6InPNo/FmJYV6Ib7/HtktfTUyRkKoFYNkF5fAV4/2F/0UFXARX6bObvYcvSYvaFKAnIF5+Whvl0DRLXm9USwFdTcuV/N/VrIbHR0WLsFMR2+7ew3FCgW6DzwNXFx66lxP19E3wRIKvP0ZXwp0HMs4lN7+8xvAUITPQnk55VsZbcGbEntGx7RymYFJ60mMDZicZPPGZvl5icOpG2o9mHMj48qjIHJrBNh3fhtlgdBM33nhkRM877au8BPCclAYg8foPcf34HQP3CQC9eU5G/befESleVwi2S9dKcICSOtQZgw3gAv0mOhXrx0TVgz6CcHOW5Rt19Oz0IPfHocjEAI2fro0+PE1rpfcCSW89r8cBVAZznWojSRFmSw5kWmWZoSgDANc1iF+mntmg4qWne1OSnmteRXosX8KEQcKl/RXHsOW+IdnXdKYyoyBIdXWzHQenk5A8VELPwMu2Jus3jlABFQ4il49Duz/Lr8e1xr9KEAjqYAN97XUc0PqHggZNiiWOUi4ZPQ1zUIAmDMIvJwkFD4z2jqTz7iA6aDVpqzDEF8XKLnWFRYmQSoYy2sToAMPRLBYN7skRbeKnxS4yoMBarRW6FMg3X5kEIgI6PUNDhSG47gYbwbxaaqPgrICH6RAYxCAf3tr8etOyS+D+EaMT2P8FZSJi/hJOSXwzi7KOyeatm1wbjvR2e5zMHv464GnRzNvTNxD9oxM3eVLY3itDJjmu3zPB++AsIGZK085mvvZvV8k9gwAuiWki8Lrf/7lMv0/B5vbGtQ9FJ/8ZeIkoAkcWX08XXiN4JezW/GhgOrK/m/tP4st5dK6C+Plk+er/MDINTl7y3tX3i/ferKr8ozkrYYAA5K0qp3zuszhlJ5LZIvM6aEn3ePd6A+0T1ZqDmKUWVLWWL0CBGsjR1cQmX8OACOXt4T3g2Pvt20tPeh59J5bmWIJgLpxTZSHn4M9LWdvJVdrx1SwkKt+YOSStI23hJ3qgP1iQgE0bwdNxsy7Wd/p/qEAyTMRxxOKyww/9IiVdVJjbdXSisiu7m/nfeVwnzpRPtFj5CLmW45kRJVi87arHu+5npq5gcZ7qTWsrxZqAjD3i1BkV1ZCoRz8A0pXzjbSrqQUF4EEcGFmKbOB160mrdOzBey6cG0alNinnj1r0+BtppN+p59/V1hrTiIlYH1uqp84CvhrQdLqzKzh7o+mU6007RhvBvzYwS3NN+jNLwQK/OZcoO2pplmqToDLXApl8DrPfObuxaOJfQsV16wQ7KxqbxlfXf5Jp69eu5B+CoxclbMd1eeOtm5oOLuvWCd5m23udsMfkZFrcnbxyRtHt23QlkCAa8DImRUJ+X54WlOQmKG2+xeTAi/s7bi+FxYEP/Zni+rm48cewW7HOwPfFnlB7mSelMwssv9qz5nNyCYpwKEmia9TIBGHe7WMASCpqRsYdOqrPBEV2tUrRjjRX4Uxf+GXkFT3bWxknRXA1auX4ALB2wXcB/z52R+eP7AZVejq8wnxPBLaAlQI+0VHAVB6N+fKffDL+xHxKj/fyNiktCq6KbomY/GFKcDXVR9pRw1vOBfJ9DpktIlm+1g8kevYFd0AsN5mZx/4vqEEqT4rU5MfTIaIphbuv4t50CwZIyMhpwAAIABJREFUOXjbBdDJCzmxqp2g8u7QlKotUFcQv6zMf+lBZz9hbgyyoO8QwxNVOZ9XkkzuPPMW6ACs7moaKzWIyCtTNyV0NxmD9ZmFQSgneFNhurRX+VaE9PRK1w5U0sgJcyiY/IcnU96wAQ1VPN662z8llS2qNpsu4d1V97LnZMMujyqav+Pj2LYzbT3pHwVNH4rpYZ+pjub+FytQ1ak9iKGuPVDvQe0XBThxQrs6EEd110S1Bm2/LS8KON/4unnyqYr//ND72V7Rn8CKhWx3fGyAXB/ITj6uR7JD7kCArBqW2+kho22jjO6BiPOqc8ULNTbr1QOGrcgA6Eu/sXSdbjjdmUEMGHZtUQV9HbjdjIACoQehkaBljlKRDMcjDjVpHIIfASd26rAVhPzelsk7Qw9nSLLLmlPqUQvbjJwXRse+Mn93NpHt3zNGXAmlz99vnCGyejwCeRQkY98w5ELR2L+EFETzkoBpgljjwgWpzx0emiqQNeFTjX2SoG8ElHocNYV5dx4Wwm2sf2gQgkbhYe9qEhD9lxojtMg0cGVnBvE1oXBNUtPszI9zRPxFABEeFO+W3dSZuSpvLizO6uuGlLcVbJS2wZkf4xq8bNYgBlX8Xidu3yIFZfrfRUd6rrVYnJL5MmkdmyIQTxj+mR69Rr6/5ln9Rz1opdyBAOsPOfTDdnsZU6DkX35PvroU+GGvKMR8QbwcD624dy0iAahMvHjx5XduXSekdBHP96QuNfVdECQnLtm4/rcYSRNGhMJfZ04tzJCTqqZfSrTUD5PZvr+HstZYwzA4Y/SalKLnuxziRCeBkiSYdFbRcovlUWkSMF1Mxybigo4ad/HhHn0CWm4ttrEyfzuBwiRI0v6hlg7wGDejrTRLbHfQcW52Aek9IclwHEy8QwgmtNGzeej+qW3w/naXh9NeSJgYi0kpy6Thq1cj/SRI1CL0klVyY1E/xse/J6Qs/kvaaeDiT+PFXeGFHBf5eOQHQgTpYv7fQraSnhHmRosAI6DKnvfT8+7BnnlPtpiICoOiNP3FmJnAAKxbX8QMKK/jE2PWGBRA+zNCCsxM6AOgaTh4s7eW/8+7dqGFsyw3hK7RedutOZYlgvgMOsrWjH40PYuaczD6yoKycClQL0NcnGpoMMAAtyvMw0tSZMXTIEncOgQGt4bo86jLi4MznSvYE2WxMupRKLqOEBIcJXmM5t9wRGMHWCWy1qRyvH/upzskMdzFzogpFj3p6S8K0EyND5EXs3mtOAdGJekMB9dn2kAh3vJBgFsg5dvPMqXA+J/efurZtz+cYLf3w81etsm9L4yXg5/adfBT4zCSyfOnu+QedGQzApCe0+fAwSKSZ936kcbTOvxghh8b2JihV3pr6KZuK/Fvat/YKIcE0tOC2ryY4eUWACl79r76rb/pqooMX6bb2wZ4DNxwRA2GUBHB/6aBZEetl+6eAmWy5ZNQEEawtpwdbpBaYQvFLCTcXILsIRQUyJHKTNAAByDA/VRFHziWR/ckpXg3/f1z3zCpcluwMcljgNNtCJZJ9DRkA1+DUH7YjFlxUnUe5pnHRrLLxDitQlq8OF5KJiNg/7YAiXwo2p2gVcJwPwOviATeLyHyDSwQ0ll14J3+ox90c2gmulqvFZRtdWidQ7e59NaBv54nr28zCfTQZslvjpKKgu+buzgMhNxuaOq0BCCBprMc99AjZjDoh+x2TCkwNvI3Pb6O//mYkN+XbtL30/aCeDmBfK5+st+9Q2O3mK2dbFN92VIrj+wY2O/2M4wQ4rI6MyN1EANPOpie42Z4Pzi0g1f5ERUD+spdqlcBYAMfjm0kl0qTc95VrhSEsHuA4LHqT4oONakYkBCWAJv9bTsulZafVxQMtNYL0WEQklfXCSL+wOVO6R4zfXrf7UyVUDrAJ64JTlUEi4OBeRXYeYQiY+ypBA7YYIWdj1AOCttTFZBM1Iv7xyrjq72sixkGwoEwjRpgUDOUl4ClINUR1f6pRA4zQt/cU33ZdAh0rn+6Ua6J+ynMIwSjvS31HIDzFDW9l5eAK18g7E2LW4VtFOS+1WB1C2FhY/suZWXXX4DQK2LpHk/DZ5Wfvr2tYUreAsak1ZETJzhnt2t1PhUAQs7K//ziiV2Mq1qY3af5TtSROtfmJpWdQyi5j2xWMaT5fJXX6lw+5koB4Wd/SeP2AIuAFuFae4fE4CeafMNpKoYwO7CHF0SBrXsObCJ/u6l+feDHXvEVqTptVl4lrL2c8i0wGBPg2Tn7DfFg6F4O8d9K/hOj+VxOn56coxyLCe5SsqyUJl+S49aUHuLOufUFRpGlZxxqC1amn9uxIlnKJEMt9NCjoCPEfabnkNtNA9ChE+z3H3irGmuYeDPohUU9rck5stVbYOMPA4CnwKh02csP0rxo/urqnmGdN3M+wY0xZ8Sm+qTs+eDQZXmrmBKH941XwZ64F7DvQoSVbDBGb6lUkF8/HwEd+5qoaSEysbNIgIGZPCA0602qJpC6bjTDawW2k3JrkyCCuSckyEcmTwSds0N+jSiHGlwUqK3a+1oSWnnBn8vgNIA+X/rabrpt6ptqNL8wbA+g1XPRcIACby1GZsvuYzS4S4/Rui0wiltPiGwUbhufo8e+OBgdSYfF5vVnlLwivJzWPbL1YgQF0dudEM+t+KZBDpYJnolmQuKXkiuvrH5a1mVAppf2SIVyaY/ol57ucyppXojBfI57yIQ9PK2ciQ2TU/qIs6rhRLtrg/Mo9GC+mmJ1xH6JKwXAcf3nlOpni55SVoE6aV+XKA3D51O//vqr2vl//wvJTn5zD2adu32ECYLXnzAsgH6C3Z4/Bf6jd59uMvwW96116cUmCkR5mA5caeq85Gg3/qxbhbFR3zdxWLbAN0pZiX5ibJV5vjBhWhUpuJnDYmwovu4cMJOUlMZ06RknF+ilYDDiVnqQjNFlllW5uMgwz22VrqF9F9JE2ilyQmS/UMN1BGYx05XNR/zJEE2eg9i4KHzdzrVtR7zIfAF3/3PLfKdGOA8l+/EDfLB4hFOXN1jaPwwt7w0ug939YyFsIqxcvgw2am/ht0sB+HHHqlkm9mk5ARl7fPw5/numiRDGAorfs7KNtnAq2EHaOCp9iqfRXr3zFAMq9EJHPXYKA2T5KgOjeA0e2vlk+mLFcAhmAL9R4+9VBDTQC7cddIYzjcMctOKTdepksxdcmiabAwC4H+iT6GnT1HNR1shXAZdS4AETnXhOZKqQvlDk6HeyvQLruzRdW2peb+pB8BQOFoW8c3pjgKtGBCnw2NnL653mXs1UMr1Mnd0JKvpOW/u9vL6kRd3Nb1wup9niZOW8MP95WIBF/TaegMWMcvkTcGj7iE86BeIgl1MTO8S2SY8w17oy+Fh/2nJ5+K+PyuW/cV4engo2xOJSwObli0tve7VlQYE48PJlca5F2ITNy8MTmfLyhfmxh1/EhrApYFPApoBNAZsCNgXiSwGbl8eXvjZ2mwI2BWwK2BSwKRBvCti8PN4UtvHbFLApYFPApoBNgfhSwObl8aWvjd2mgE0BmwI2BWwKxJsCNi+PN4Vt/DYFbArYFLApYFMgvhSweXl86WtjtylgU8CmgE0BmwLxpoDNy+NNYRt/5BSAhKZmwHMDbTHLnz3VHzNUZtu17I/lWUQLzXRiQnj+6R/qnHddE8gJY0TIo2dbs23TU2x7ge+hhZbFg6Q6UZZoW+CO7ek2BZaAAnbetyUgur2kCQXm+nw9FgXEuoN8dXMTLNbdchrRBeTvlNKW6auk9w9d/DGVqTQaJh2p4CyQmfW75yPI40bPR3OuQc00ZkX53FhxbmR2pm01OwS14Qc7glxCU2sy6UYhy33jqgj3llC6qq8i0MvtDa4vc/ml+qToRE803aq0mTTXVwF/FUqqWil/rShBrGgy9GERW/iY7E/JZ9MLwhUhaWWCyRy726bAY0cBm5c/dl/Zb3zD+tzj0nGRdT1wmSTdlNlzZIQB/LU1hPRPD4QS5WouBAuWtBirp1shHP78MOHYeUGu67vAqcNQxkMqsyZOR6pljKfipp6XdMiVS6xWlsayEpJIQuJaIyCtv67POY+14XWEnQ3NRVPsJKG4zMCJjYvLPYmeI8mn6gcHCpl6J9/0nWoSTnjAgQlBsEoNmxk+uVwmshia64UitlCkh8x21weYgmAyiI4m3MSlbkydbZiu9tmFTJb6e3hs1g/Ly7U6aXAmWs1s+moX2bI54kTTy4cUUkEzQ6m3hW/w6sGc0gsiNHQtZlSrn6Z2CjPLinA9CX0rn8si96M/qCBtO/x4Y/ksyLLO8gAGdUFKPtPiMYhqjqE4SEyxUVRY59sHJbpT+DoxSnrwrOQ8EpzZ5K49thLBhUJhFOImzXmeRFExByHAutYV1rICKIz230VtR31Ar/NYgHKCXVPwnvQMVCD7HV+FzMg4pZTy6o1KgGehXVi5nBipKsgkv9CVYjm/0+d8/VNC3vmsOmKsND17v6ESWsTzbcDlSYEfPiz519peeW9QJ82s4ClAWPNyqDVeTb64+Ugpsw0caMWbBNjPFqVneRLAYlcbM8JXmbCYLhrqab2ABUz3pBKC14Ux+R1LuU+OE7dc9dVP3u/xq0VOobON7BitobNESJ/YvkEjy5FpkRwFTfqHoA5mXsmDlopes2qYUWCLEDRpVSJJdkp8NDQzRRIdyMywZnm+pBI/4lWryIDN3lEQRtIFVhcgeayqHCuJHX5YaCqVAusi5XJ9z5nORuJBTQbys7ySQmdNokM5iHzRKVsJ2medbkDir2HV6TIGs6sSVjGBO4ToAgSWe5LLX3eUbcX6U6pcXlv28GLjzGuMUmeqdTiUle3V3Xhivfr88XkaJnqzX3ENR4EhLSMT6B3FBBt0+VPgm+pnz+UMXP/VSbcK9cvzi/7p+H9//YcXhFu34uVXD1bf8F/pYdi2zJbG7hDCyeXj5/ztHj8ys+X8bPY/Cvpjv8GuTvKFiCWnVjaV+tsnCZAl3ePd6A/wS0+PEu+2ZU4xfseL0zJKb7gu1bFHugFkIVifNLWvd7DEmZ+Epuv5m4ojXRXgQK+usueHvb6+Fn5uiL+miIp+4kk7+1Z7aA2x2eBsqGNwYC0UcVdsvSBhjwSpLZxHTVvAumiNNWyg1bxjdgZq0BXeG11X+FoNAYu1Qy5NNtPXNJt3xEWvGgI8RKTqD/kMOuqSNQngq8gI3wJTRVbCBK7LrQLXtRnOms6Ozgy0rcyX98n2m72D2mOQ7GaU+SpgaDqABXDhG0n0JvdebFsp3YrgFtLSAdZ3yRSiQj/uL976e7/Ux+cQU2d9XSUNVWnxwW5jNaHA+J/OfUzS2yVGDjDON/7reEfGoS+u/OHwVtEUC14+PTpCNmzheDZgSN9Zs+ugHtP42Bh5Ysvtbfaf0dNDbqfv9O+RXlNTN5CBy53Te3Yq9JzsvkxS5VGT6Xb3fCigMHLQ3ILQSR80XQObASW5QN06nzUinKO4awG4ZO/XpFi4Xtx1CjlWUkohAcYD4vgcNXK7wGNrqrF3IAnZ1eyPD6AKKuVJc7pNAItCIV6WNSVu7QXxd6oR/Azwr+53wd6BECKZauwbBO26hVSq073j5vV1Zqn4vgqM9uzDmyroFWq7VJlet9tkl/DsiCsxlQwNhHIjVrzP3R+ZHfQF7htcBDrBwaI5V9I6JJS6XI2Bi23u19ZOgsKGt76zJ7DfBRSYHh0iJYJ+uyuuFEjPSCfkq5aOw1sV4tMe0zUteDnOuTExTfSmcfeZ93h0XX6wFr8P/kT2Y0oBd9kO8gmrz5i6vWFLpSn4EzoAv8tkITp2KhcCBxVUIpfZDHD6ClCQMFw2pqTGDZCFOttLjKezMXkQDL2U4zpq8n463DuwO498M5u3XRX99VsPNfWo/mUo9MPc/qGJl3Kla7ajbE2vb+h3RwiVSk2R6JHOtx3+xmCCOaH0+fsQAmdqR+CmofoBPeN0cjkI63ed2s0Jp8AfAIH7XJP4z4NDajdYCnT63j5Pjv4722W/LwoFSg7/+vNhdqXxsXHiKslgu5h3i/jylG2l+d/6t7rPTTPw3Cuaz5PBgn6JkIH9bvqenKPBg/EYRul/73bhRCgWLveUnx9nMTGQKw72sCP4DoZnBQ9O1wDALw/xv9sFdn26EKBVgTUwIu8TYXacnuTRy0v7r7LbM4IRFom6ogEbj5ttpWfkkwudsIr0XL1KyhjjhdJtf6K7dW2z4b+G7LyqVIcVeUAKDPQmu2v5X3D9jILc2obsJBJsqRia0o9F0UYpX8OAS8MVAf4DZ/ikOw8VzLCK3H/KNxzCRZVmRZ/eDY1f3FFTSDqCedtVPfDK/N1rRn093SS7kHJ3HhxbcAOQ6QYHBGMwFXynyPOaZTopxVv1oKU+mHfExB/QiHTePaHp3g5z1/esZ3RGen6dROe64UCbTpTnQeQWjTdDMwo/2n+P7NadEbh7oOVOdnEJfAugn+Dhl6g1dXazc/XT8n8H4IopeBiYzU23BQAEvN4UJK+cndBDgBudMuoULsFMd672dTPzuw/QvaEX3rU6l7xPwRLMFPs1jhToOJZxKL294w2Q1oWPlVyevvPCIyc4c21d4SdE5P4N5vNHKKMDK21cLzl/sYvI9mn0hL9FeWF9xpVHwRTg6Nn+28DLpT3RZkZb8KbkF4Z8t/zQcEulvGPZm+xmj2JaBgD3uSs9qKxO2dNyhZRv3f9mJ0w/A1zcfTx736Hh4M10fO+8+p5bwinvEzsN/8PAJnvS3O7brQdzbtDtwQlwS/vOb1P3QDdfSs4+CrrxfIinesMXV97/cOutKUKUjeGQ+SOZzEcnyRaE72kdSTtgDvykjqAyXHz2pBSPZHOVnMh4a6vih+XV2XnA3izAhm5ZqzorJn4KEWowVvzMdaAjmoyrjrRUBKV3vHDUYEqWBBQftfAz4PG9yasd8gRG+hfp2FW0gpfQQ0hnE/pyurBAYedJKa6S4RbwVAnzzA189MB1TNYtc+51sj9gwmB973N6QZZHGqm9nJ+ltWAPcHdJAG+s+T0QEdDr40PahIgw3gzi0ww6Bj5Igbrggf+EtxZZfkp+GZgM0PYv9lcQLhSHTuCUb59/57N7XcWIfKJp2wbnthOd7T4Hs9ZfDzw9mnlj4p4T+oCpe1y+tHsNFJ4BSvNdvueDNiBsYLrlKUdzP7v3izQFeTO/BOI8eu31P/9ymf6fg81tDeoeik/+MnES0ASOrD6e3ttl28t56i5aS3Vl/7f2n8WWcmkrVrwcISg/puy2esUFMUeP6ExX/cDIKQMGi/uFRzuVSZPnq/zAyFkH7yvvl2892VV5ZjPCjE+MkSLORwwE3G/busd3KsweI+XodLRJ57/ygdavrKF8IoDZc4lsual6+Ul8t32yUvbmmzxffwHCySgjBwTg1OYPZF+986jlphk6QT9rMp+cJKXF8mVFAPokdaF5W2aQURy7qeciUdKk9A+Bnbi22RCdhejMREPWSY36mXNrI3e/vx2NzdqD+9TFpCV6jFxEm7Cgt9m+v5OqwuJv+gJayheIEU9IygqTs2WqER3BHIbFlesODnkUZiZ2M4S5kdrLDcvQDvQeJ2hJH/0o0D2iV4CjyX/d85ZyObDZ1ZlZw90fTadaadrRPx/4MZzI9KE3vxAcpzkXKHAKPf7ga4XLXApl8LrrGnP3MsUYowFUXAMjVxmzs6q9ZXx1+SedvnrtQvopMPLL1cjI4XFUnzvauqHh7L5ipUfqN/33dsMfkZGrS5DikzeObtugLYEAyMiZFQn5fnga1jJFag8sPgVe2NtxfS8sC37szxbVzc+PXd21zH1RRK5eMcIIzSpEuJdPiOeR6vbFAI93Br4t8vJcLSUzi+y/2nNmM/JOjfHj6qDMp0+RifQmD8/j41VO423F9eeBXJqimczHO2+v99jGckoWUHo358o0hV/ej4hX+flG9oN+yIpgakb4glzF4QtjytkgLkcNz9VEMr0IK5rtY/GAOpe7pvDNZPlHWrASqI6fcR1LTCAJ3U2TU6WoLgbDMDniei0JZMq+zpf4e4aCAbQCEzBE/fapDl9iTuiDBn7j2nUHNRMpSF7Jr94Yfq0gnM8nSP9BZ+3uh5jb55gLTNQtYKRvTuVQdZgeQQFbmboJzm4M1lfG4ROEcpL9FjUlML3yK56uaRZdHZu90r0BlTQlhdr9TP7Dk7Uyiyyjd7d/SipbeAnbu6vuZc/Jhl0eVTR/x8exbWfaetI/CgpN8z8chhBTHc39L1akMD0wETDUtQfqPfj7SQFOnNCuDnBd6Jqo5ibYjWVEAecbXzdPPlXxnx96P9sr+hNYEcVeUUb3QMR51bliScKOfO6uLYpQy88ZHxsg1weyk4/z3aAA0DpAqY6ZWIqo8lxSgLdpo4vxllp5ZEdO6UHPo/ekU/Sc9A/s+kJ8Iov9oMncD5r/daNtJHOnBeCTOhR6EBoJWuYKnZvqJ44CoQguE431/9LTEXOlERKE/N6WyTtRv53sYoVyPaII24ycF5WOvX/y/vZcTGVTuCap6QEuBuFVQedraGWgAivq3ldhP/v0D2Hqug5ICAPrSuYGaiQeAWmeCMliKpezaKN6h2NKaWhDD6V5YPjP67grBdnTHpqWriRZCUtz1ZrgT8Czz878OEfEXwQI5Q+Kd8umBAaHypsLi7P6uiH1bAUbpW1Iq4f3GC+bNYhBFb/Xidu3SEEZL8KANJyeay0Wp2S+TFrHpojXEcHWpkevke+veVb/UQ+rCBEIsP5QJKj0GOz2UlGg5F9+T766FPhhryjEPBpejgdwH/DnZ7P+2As/FTBpxjKtwwfq/VLUb2tKeB3A4jS31Bza6KZWBroezX8X9cqKyfzOrSyPHY1mJB/kDNdrd/W/xTgpjAhlJWXSKKmXQN61ekC/HcpaYw1jNX+hY3MDPz4vOweAqrlqlQNEqFY5rgyYIgqs34jWYDUcdJzq28FIjOwt0RiyVXH3uaQ5QCjANT97uaxW0VmvWXMGLAU6j4TnNmFwoOSdJ1hd6sLEO4Sshe3BJvXPTzSS3oNXM93DWExKWSYNX/2EzksAnRuI4UqkwxeX5vj494SUxQU1h9ROA8eR4zfQeCHHRT4e+YGQF4yHseDlPe8eJGdkSdQ4MTY9KKpa6TMlCfiC5MIWmyXng6XnXfftI4p33nwQSHMkk/m+xg17Qc9gPzoKYM5wfRiriDHP9s+EEax1iKNrUsFxu2upePls2yQpVIwOkDMOdMhMXBkcBf3VUUCfCXssvYnBMOF3xhSwEkwk9nKoNYd565jULqC6P2ZYg+/AELKsNW+tfdDN9ms58thec0dIEhwFbwmxvM5iYN4hxw6sy/H+uZ/ukMTtYS52DIoYvqanv0hGDfimxoeIXivOAUUlSaMQDy7H5k9UUr45GnskXhQY/9PbGYfIqQFWo/7DzV5CcsQrWsSkrVs/0qiP4AIV99hAmDSok+dPd4kXM/aiqHo9AMnRuCcaDNzEeDXWrS+6VKqGvc1/FTSZf3udrHfMH8VvdSb9lc9+66W7pxrDcKmEgnj+/lIrrFncVzjiI3sIBQVypDIRld40es00Ji2hVJ8mhYsrUxCF+aRh9LDQPOqc4l2BSXdqttC8vgXIYEOKdxscIEIPfzJbRtxvkWpGOIFmsdUC/CQYqiHguLtwbjw6nVUH3uk/+gF3oSETXa3XCsq2OrQFh25zQWiBv54nr2+L1FXIUVJR8H1zF4eBQABbU6e8gBiABJqMgW3aljgMbLf9HnMKjI38TY+z438+JuT3paDXEjwWvBx80DBqXIsXp7Zq0Hgf0XuxAavDpGYU/fTpfbczNwtWEnehTzjZ72YCtSHiax/ZJmMAzOSTD7VgdLCdZ/sHKKqe0+aB7+K15t+LofYb/WffLyLk+vFsiamLYtDDroB6CN4tP+yUJwNA+ZUHRTGw83kxoVgQaqYTkr8amU0EqEFhe6oCfMi9uH+s9r3a25zrUCeiuxk0UQMMUeDlJWApSNVGVTDRCxdXJgKgfVqMO94VwGhNY/Q1Vy/TiQsduG91d2GRwz0GXPDy9LHgCPLgfoiFjPG7lJVdTwoa9RfjlSJH52n4rPLTt7c1TMlTMCatjpw4wTm7XavzqQAQclb+5xdP7GJc1cKsluY7UUfqXExUOoSS+8hmFUOaz1cJgeNsTDmEn/0ljdsDLAJahGvtHRN0uYkm33CaiiHMDuzhBVFg654Dm8jfbqq3MfBjr/iKVJ02K69ioWMn6RmH2oKV6ZjgRWKfkgOaMegrZc8Hhy67aRg6wSojskqc9Tx/M+cTPJfA8o2e6hDF7s7ZLx2cM5/LEeSqcxwYqh/VQHg3MFRY6BhEru+/TsibO9b3HCP7qnEJtz8z6Gl1H/+WkNJkIhnaZe85Cb+0kBour2yyNPmSbAVXeog759YXNyE0Dmz2VeQYuvvtvKnauTFOr7Fnj2aD0ArKwQVov4pfWlT5F/QQu4gdjaaQQ/mEHGGQL13+lUe7byrIr5+D8SULUpFH+YhM7CyKPLbBvQOz0ZtUuXHTBprhtQLbSbm16G3eAwHWhnxkMgrIQeaQX+dmQyupS54pdvEALX3GOEInFlYlDIIDuYlILcqmZ+FIL15T3Ntxd4qp2iKGgfouh3tGNylhhDwQRKnNgFHc0qWRn8G1qMs6pGXVpX6TYeCOBSliaFZ2fhb1ivAujVyOO4Ho7U6I51Z80wrqbkzwTDSz7sblkiuQB6Zf2nhly0R7pEK5NAP90tN9TtfTdVLbYD7HPWTCHp6WxrEyGxPDpnQ6qxpOtLs2OI9Cx8tHe6WAeGXQ/owfBcBx/eeU6meLnlKWgDppX5coDcPnU7/++qva+X//a2lgUeGerBea62YvMnXuAZZ/VfVs50bshgUF/qN3n24Ufov71kpZu3UjkDncvKy4zqZLp+LPulUYG/V903uBSYsKfKO43WB8OdZrEYnJ3eJ8AAAgAElEQVSV6Gc+EFLKn6jTaAhcOIGT8XWHJb58JlwMHjJFWpA78tzyOEUfLg/11gy+YOrGDS/0siLwXZA3I/BG5Pz2DWshMYP8KhId6EL8QMQt480JsXE1dXTfiOhPKOLlogPc/c8t0U2IGhol+/EDfLB41EiW44S0fxhajttaVnv6x0LYjs3Lw38nNFUOVCxliqFh6rfjG6jUHn6+DcFQQMfL0ZcYanmZRAkz85RX/DnW1/lQxmjNcmteLioYSgU780rnEvaIGK26kfm8gE8feJNF6HMXSclUZRNzUCEtv1TnXq4MRvrJ3CG4KcxdhO2HrwlSBewmAUxeawLDwsfyXd2q2bpmV5NYbkKHK/a8nBYsz1QyvWDKttYKNWWbbvXHumnz8vBfn83Lw9NIhaDMG/T2yoOmhOVe41XZ67L61PHyZbU3ezM2BeJEgdjzcpr2tQ5i26QHNPAno9PAKzOX+6fNy8N/Q5SXW9nLw6N4ciBSK3uCSpKFJ+fU9kltCtgUWKYUgLSvv1Qt073Z21oCClj4sS/BbuwlbQrYFLApYFPApoBNgWgpYPPyaClmw9sUsClgU8CmgE2B5UUBm5cvr+/D3o1NAZsCNgVsCtgUiJYCNi+PlmI2vE0BmwI2BWwK2BRYXhSwefny+j7s3dgUsClgU8CmgE2BaClg8/JoKWbD2xSwKWBTwKaATYHlRQGbly+v7+PJ3g0kNDUjACQ8CVNzxWymsR8SrRg7F7EnlmcRbXumExPC80//0DzqrMgoIPGLESGPPo6tENSbX9ADuYBohvwFIbEn2xRY5hSw48uX+Rf0RG1vrs/XM2h+4u5goVm+cfNJ+hE5L+wC8ncqKb75el/9Qxd/TGUS2IVJRyo4C6SWg5ooJgnV9ceg6UhJlSBlHlacG5mdaVvNbIZAbfjBjiCX0FSP0bINWe4bV0W0N0F+VhWzPhGbmJIquPSSNNdXAX8VylwJvyChrG6a2sQittCY7OeT7MIVIWllhFn2VFz2i02BZUsBm5cv26/mCd1YnjBfOrKuBy4TPiez58gIBvhrawjphwzqiWpy9agw0HWGPz9MuNzpBbmu7wKnDkMZD4nHY/ltY0EpLWM8FTf1vKSj7xQUcY/kyYKCLAmJa42gtP66noZYG15H2NnQXIJZ/XIjVqgTUxZxFlgRowUK9yanOljM/UMtHSSp5MFkP8kPU4wccq0zReeAr8tEZtGZvEMRWyjSQ2a76wPdBhAdTQzjS9kxdbZhutrHlM9Zys3Yay9/CoTl5Vr5LzgMrSQ2fbWLbNmcsvzPpt+hVADNpIKZHjjStkIfPdqed5Np3Tau7FukSK3g4nIKqwUXcWzlc1nkfvTrQdkxZM/8EybLekFKPgPPY8CE3vqiXigOknK2jCkzXXp1lGUn+Yb7+lP48ppKevCs5DwSnNnkrj22EuGFQqGICxrWkTpA7p94zsiMgXWtK6zVsUZaVI3UB/Q6jwUoJ0x2FXk3lJcNxp+VYuVyYqQq1Wok6m88kW8+zpCdPufrn2LVsuqIF6Lp2fsNldAinm8DLk8K/PBhyb/W9sp7gzppZgVPAcKalyNDIlBmVCkRBsVDV7xJNvqvbFF6licBLHa1MWOdxWj0Q1CV9WbmwZzSC51X33PLxV4BS1cnFmDVM/jo0ZvMiPUpTJZZim5RdU5pH8lRbKd/6PMmklfyoKWi17S4WRToIgNNWpVIkp0SHw3NTJFEB5bUxJrl+ZJK/Ij3NYXLRlIcBa4jAZLHqsoJ1DcTlYdR9gesi5QfkwTomc5G4kFNBvKzvJJCJ1OZVL7olK0E7bNON4BD5rVTlYVoGZsmk4I0JmVnk7SMo3C/4SuYqXhj+iJVLq8te3ixceY1Rqkz1Tocysr2Kt9FTNeMBTJPw0Rv9iuu4ShwpWVkgrIpigk26PKnwDfVz57LGbj+q5NuFeqX5xf90/H//voPLwi3bsXLrx6svuG/0sOw7S3v3RzO2JE9docQTi4fP+dv9/iXe62Rzf5HQb+QCrHoHBudJFtSZUzjE2OxwCnCEd9TiFZc3D6xrEZ17JFuBGVorE+a2tc7WOLMTzLUvowUUbRwoFdXFdEPe319LTyCEC8ZC0qFAnxourNvtYcWjpsNzoY6BgfWQhF3xdYLEvZIkNrCedS0Bazr/naXpG9Aq3nH7AzUoCu8N7qu8LUaAtXSHHI9upm+ptm8Iy561RDgISJVf8hn0FGXrEkAX0VjCXCjHEywfK0iXcD7INnt9Rgn4l5mBtpW5kdeNw8rowM2US3a0HQAi+bBN5LoTe692LZSuhXBZaWlA6zvvLuDiAyPVZ+3/t4v9fHZ8dRZX1dJQ1VafLDbWE0oMP6ncx+T9HaJkQOM843/Ot6RceiLK384vFU0xYKXT4+OkA1bOJ4NGNJ31uw6qMc0PjZGjLZBPdRvt53x6q6iS7emCJF5+XR7W8auHQMomtvPIlNAYeRgCwehkz5ougZeAkpygbo1jttT3LVgCcner6no4Xpx1ynkWEkphQQYD4jjc9TI7QJb8lRj70ASsqvZHx+QkkLKk+Z0G5eFeFnWlLi1F7T9U43gZ4D/F/8u2DsQQiRTjX2DoF23kEp1unfcvL7OLBXfV4HRXvBYyuXwRUy8ZMbIAVliKhkaCOWqrgwC/FzX3P2R2UFf4L5eYT7TCQ4WzbmS1iGh1OVqDFxsc7+2dhIUNrz1nUNnN4wUmB4dIiXGbrsnvhRIz0gn5KuWjsNbFeLTHtNFLXg5zrkxMU30pnH3mfd4dF3+0gvkfYPBkgf6bbfS1meR/Vd7zmx203PeuZXlKSOXbF4e5bcOv8tkITp24BNU5HI5DAvLFnHg9BUBojpFG8AW2IEbIAt1tpcYT2dj8iAIuJTjOmryfjrcO7A7j3wzm7ddFf31mw019ZxqkjtR6Ie5/UMTL+VK12xH2Zpe39DvjhBKIlMkeqTRtgtya5tzpUnI75ly8rNt01NkdUIZy8glZwLm0gM6/9Ln70MIXGTebah+AL+8Bp1cDmjvOrWbE24H/gAI3OeaYC3Bn0e0p3yC4Dt9b58nR//9CTrxcjlqyeFffz7MbmZ8bJy4SjLYLubdIr48ZVtp/rf+re5z0ww894rm82SwoF8iZGC/m74n52jw4KUFo/S/d7tw4vi5HXJP+flxFhMDueJgDzuC71A7XMGD0zUA8DtD/O92gV2fLgRoVWANjMj7RJgdpyd59PLS/qvs9oxghEWirqhhy8zIJyOT8qG6Om+IrPLa8blTcAdUqKecKBk3Jj1Wp1BgiDUlDWcc7+rhvggVz9K9gI69ttnwX0N2XlWqw2pXIOmCp7QbGIkVGHCahuwkEmypGJqywhZmDHj2KQ0DLg1XBPgPHbPvPFQwwypy/ynfcAgXVZoVfXo3NH5BR00h6QjmbVf1wCvzd68Z9fV0k+xCyt15cGzBDUCmGxwQjMFU6J8iz2uOeEkp3qoHLehxZkkiI+pIe4CDqgfEl8+bZgnK6HLn503DLRWTs4woj/bsEeDEqair155E57rhQJte8aCNa2803gzNKFoXvvXfI7t1Z8S9tdzJLi6BbwH0Ezz8ErWmzm52rn5a/u8AXDEFDwOzuem2AICA15uC5JWzE3oIcKNTRp3CJZjpztW+bmZ+9wG6N/TCu1bnkvcpWIKZYr/GkQIdxzIOpbd3vAHSuvCxksvTd1545PSveHPrCr/YjQvM549QRgfG07i+54LeXi5bdtHT+xblhfUZVx4FU4ClZftvAwuR9kSbGW3Bm5LjGHKs8kPDLZXyjoE5vTn2fs/NHsUUDQDuc1d6doLaEPzOrpDyrfvf7ITpZ4CLu49n7zs0HLyZju+aM5q8T+w0/A8Dm+xJc7tvtx7MuUG3B+fBLe07v03dA918KTn7KEjFbsRTveGLK+9/uFXVq6d7vBuVQ41PkFc8KYS3mMO2Sy+82ha8QI+JNNGOicXRi6Hncql0LtiC+0zwLCmfPMDsweoUMEPatt+ckhLAWI18CoI3oezrsCW32R8HxbqY/6AyXLxeUoqnlI5ITmR8XDXKf+iHpY8BA3uzAFtSymvNq8AJ/KcQoQZjSTQ0AI5oMq461lIRlN7xwlGDKVkSUHzUws+Ax/cmr3bIExhxU6RjV9EKXkIPIZ1N6MvpwgKFnSeluEqGW8BTJcwzN/DRA9cxWbfsKGDkb9kfMGGwvvc5vSDLI43UXs7Pkr38xHHbU41DpAb4K00HJLFe3I/Yew4iAnp9gwOFOmlbtxzwbIg3g/g05owSCB+kAMoJ6j/hrcV1U/LLwGSAtn+xv4JhkTh1AKd8+/w7n93rKsYFJpq2bXBuO9HZ7nMw6/31wNOjmTcm7qHJFJi6x+VLu9dA4RmgNN/lez5oA8IGpluecjT3s3u/SFOQN/NLIM6j117/8y+XqeoGm9sa1D0Un/xl4iSgCRxZfTy9t8u2l/PUXbSW6sr+b+0/iy3l0lZWhNkR5ceU3VavuCDm6GEwSMNX/cDIKQMGi/uFRzuVSZPnq5D9+FUP8C3vXXm/fOvJrsozmxEGnciKvNsURg496Rn537Z1j+9UmD1GytHpqakbSP4rH2j9yhrKJwKYPZfIlpuqlx9lzIH2yUr5djJ5vh7ZsKQ/B6N4ZZM/kH31zqOWmxo6RB6Q3N/Gx0imekAJAs0QgEE9JtxCzt5KrlaPCT8xoAXZzzoVdnWSvf50bQHlzewU4ShJCFjxB3btVU6BN6Gb28BpkXHZU9ZYxE/8nQ1GvV5Tz0WipEnpHwJn79rmlSIkZlHRrJMa9TPnJiN3v78djc3ag/vUxaQleoxcRJuwoLfZvr+TqsLib/oCWsoXiBFPSMoKk7OFupUJdMjKdQeHPAozE7sZwsYjtZcLz3gvUAFKCOGDmXBkEzvQ88tn3jJzQEtanZk13P3RdKqVph3984EfO4RLSZ305heC4zTnAgVOoccffK1wmUuhDF53XWPuXhY4YzKEimtg5Cpjdla1t4yvLv+k01cvWURwlU+BkV+uRkYOj6P63NHWDQ1n9xUrPVK/6b+3G/6IjFxdghSfvHF02wZtCQRARs6sSMj3w9OwlilSe2DxKfDC3o7re2FZ8GN/tqhufn7s6q5l7osicvWKEUZoViHCvXxCPI9QktY/452Bb4u8PMdKyWRszxrjx9UvyfOLTKQ3PfrI26+W0auDPMGMX1rhW7e+aOBy5/SenXdaiecMiPIM8NWrlyA+Tb2v0BH3AX9+9ofnD2yWLx/0AtF4usYtXSCuXiVlOr8EBqHxNSwl4brAElbCkL7Tv8eIazF7GPMqOoh9RLzKz7fO2mq6qYJcNdALprBBXI4anquJZHoRWjTbx+IBdS53TeGbyfKPtGAlUB0/4zqWmEASupsmp0pRXQyGYXLE9VoSyJR9neA7xt4zFAygFaBuZei3T3X4EnPC5kwVWB+U6w5qJlKQvJJfvcjtXEE5v08jU6TXo5dkARqXhrA3q5vQytRNcHZjsD6zHxDKSfZb1JTA9MqvuARo+OFozV7J/Q2VNCWFGt3kPzxZK7PIMnp3+6eksoWXsL276l72nGzY5VFF83d8HNt2pq0n/aOg0DT/w2EIMdXR3P9iBf+bixjq2gP1Hvz9pAAnTmhXB7gudE1UMyjs12VFAecbXzdPPlXxnx96P9sr+hNYEcVuUUb3QMR51bliScKOfO6uLao4yE0aHxsg1weyk49zvdDYoXVQ7TQhkHQFlOeSorhNG12Mt9TKIztySg96Hr0nnaLnpH9g1xe6EymC9eQkyeD/JyUYEbCxVB/Xnu7MINc1WwNJLX6l6Di9DcD/gD3ShSDy00VCSdDSt6GLA2KFJAHRfomRb2aekKEHoZGgZa7Qual+4ihQeJJoGdb/Sz+OudIICUJ+b8vknajfTnaJmKUeYZg2w9Ki0rH3T97fnouhZYVrkpoe4CIQXhV0voZWBiqwou59FfazD02jRjogIQysK5kbgFEFWkZAmidCspjK5SzaWL9TM4S7tkb7EsFL/6cyvTo9Ac8+O/PjHBF/ESCUPyjeLZsSmD2qvLmwOKuvW+9Rb0irh/cYL5s1iEEVv9eJ27dIQRkvwoA0nJ5rLRanZL5MWsemiNcRwdamR6+R7695Vv9RD1spdyDA+kORoNJjsNtLRYGSf/k9+epS4Ie9ohDzaHg5HoBKk6wqeOGnssyMBup9Xju98PXmg2FLzaGNbmploLNp/js9HuTNbZNXOwPEqfzvIoPcuXWdEPwhtn7obUAyH4CCfYvfGlowaklJCV42uqOSA90glhVHh5zheu2u/rcYDxFGhLKSMlFCJS+BvGv1gH47lLXGGsZq/kLH5gZ+fF52DgBVc9UqB4hQrXJcGWSeQYH1G9EarIaDjkth3LVJyN4MOc6AFHefS5oDhAJc87SXq5j0CglpIA8+QtM/vaQlzIEOEKBbOmZJx9DvdE6LmHiHkLWwPdik/vmJRtKLItQZi0kpy6ThvBM6LwG8VRDDlUi/VDza4+PfE1IWD8w8TjsNHE+Px7/1Qo6LfDzyAyEvGM9iwct73j1IzsiSqHFibHrA+E2s9JmSBCz5i8VmxXlh6XnXffuI4p1nisGRtvH68VL0JtOBgPqd3ND1Ka4A7OVctdNnRqlgB9zhKMkvL7klohPf1nedNyXXBB5i8VuYM1wfxipizLP9M2EE6wXtneYz3+5aKl4+2zZJCnOVE9CsKUxcGfSjvzreC2cUGNNPvYnBAPg7YwpYCSYSeznUmkPHOkNqF6rAl9DIunQm4Rq6nqFuRHnQ/U2yYSs92qe5IyQJjoK3hFhe1+Zzb5BjJ2vNW+zSZO6nOyRxe5iLHYckZo309BfJqAHb1PgQ0WvFOaCoJGkU4sHl2PyJSso3R2OPxIsC4396O+MQOTXAatR/uNlLSI54RYuYtHXrRxr1EVzAgcYGwiQQnTx/uku8mLEXudd18DLjR6LBwM+MUwuY8aVSNezNbBFUkoMtII1lzxQ2Zc/eV7/1N7EWdMkTbWNpMQeMynwwup8HBTtvXDdbUusPT8nJ8+/qwgtxOZpCQEOzZG8YKAyc+6W7pxrDcKmEgnj+/lIrrFncVzjqIHsIBQVypDIRld4QrHXKPCYtoVSfJoWLK1MQhfkEzzIaCTaPOqd4V+C4r3gpq28BDAoVkJhFoQNuRhQGhu6EkJ5PbPsXryr3JrtMzOQms2gWWy3AT4ICr4iE5zjubjI79t3OqgPv9B/9oJvDPNHVeq2gbKtD6xy6zQWhBf56nry+LVJXIUdJRcH3zV0cBgIBbE2d8gJiABJoMga2aVviMLDd9nvMKTA28jc9zo7/+ZiQ35du0vfTtgUvB1cpjBpXIp4RXNJ4H9F7scluXxTh9Ol9tzNZPzLaa/oP+oST/W4tUBujyfeRbTIGlGg/+VALRgfbebZ/gGLrOa3jTKZrLHwAtd8b/WffB1Z9/Xi2xNQFMejoXKZnz9Lim/1tOy6ValH1GJO2nxxq0lOSbNkCXD+wvkZnjI/gCGEoCe73qUSXLQD98/MxfG7pH8hTRop3pySAohjYeUVgHkwoFoeAgh9ghVXCwKLBCArbUxWYmhT3j9W+V3tZpTFKq7kOGrUFUeDlJWApSHVEhp+LKzOdAjpk6ZZA/4XaqTRGX3P1Mp240IH7+rsLBMUNc8VY4TttgOD4AHtLA5GdxgXozeQL3Y1ovpSVXU8KGvUnAl+UPk/DZ5Wfvr2tYUpeDWPS6siJE5yz27U6nwoAIWflf37xxC7GVS3MRtN8J+pInYuJSodQch/ZrGJI8/kqIXCcjSmH8LO/pHF7gEVAi3CtvWOCLjfR5BtOUzGE2YE9vCAKbN1zYBP52031NgZ+7BVfkarTZuVVVlislp5xqC1YmY4JXiT2KTmgGYO+UvZ8cOiyG+2vhOS/36OoxFnP8zdzPsGV2LgsbMODnuoQxe7O2S+1OaOvHEGuOseBofpRDWiGgaHCQseQI14n5M0d63uOkX20Lpnbnxn0tLqPfwsW6mQihYHJ3nMSfmkhteqJssnS5EuyFVzpIe6cW1+g/hluMFXkGHqK7bypen1jnF5jz5731sl7aCMYE5+Rv8EJrJGyatgY0dz+wVA9DFMULz8wVAsd++F/tV1FaWwMnrRr+NfqFBTIkpIIsd5/5QA5rH2b9MvSZwWgqBb5H8gqCqW1Ze0r2n1TQX79HIwvWWui3onIxM4iQaut+AGhWW9SFQPqe9EMrxXYTsqtRW/znpAgH5k8E3KQOeRXiLdeKYdp6dFatmnps2INJLGwKmHQvCaKKJuehSO9hjf8W8fdKa5qy6C+xByggKvMkQenvnw4S0CnQt3ToJiblrs+/CIWENRlHdKyiq8FcMeCFDE0KzuHQ/KK8C6NXI47gejtTojnVnzTCupuTPBMNLPuxuWSK5AHRq6YUtky0R6pUC4dFf3S031O19N1UttgPsc9ZMIenpbGsTIbE8OmdDqrGk60uzY4j0LHy0d7pYB4ZdD+jB8FwHH955TqZ4ueUpaAOmlf/3/23j8m6ivdH3/8xmRT6zZsg2lpqAwMvxRtAthS3eVyyYQ7yodPZC/VtjGS0uqC3am/2N4aJdm5CRj7MXTRkhaqlS6N2RbLFnO56CyZcMncVWkFkiLIjxkYLFvaQCxpLP7jxu9zzvvXeb/n/Z4fMOAA5x3SOT+e85zn/Ro7zznPec7zSPFcpTblc9XDhw/l2j//4feARaZbWQWimG8f8jlURpV/VfZsX1mIzOdt/9B1RDMcf4u7n8nSbpsoEfkhxkN03UdzpktpyM86EzTUZxxRurho0JtLxzdKNVw0COsrDHTp6p2U0p/Iw+gVuEm5ql9gfN1xCrxyLV3J0ycXFCGucvDRcybQG0V0p/a6POZb8/EF0xsrtNHFis50VCsPMd6IPf2ffxeP4eLlL07rqOgfRiJVgIh4xkL6RnIl3FRft+Yb0fsnZMx/Xj1v/UvLvMYHHkx29u5y9WXxwKOWAEXCL/qXgJSPVsRfZuL8XJcH/hLIFtwOaG9QtrDEa+zUJrprDzyeUzAIaHQ5/uhj4FUhgRVDZVwkP8faPB8ydWBdrpcwlG7s9AOQyZxJaJHAilYhn0MJffrQmyxIn7tgUqZKMsxihrT0AvRTm88jqm0fFsxaBDWxmGVVoFKlZxW1+2KoT1lUVjZWcKOlCUsT5nL4dTlNWJ4sRXohIdsuF8kh28Is/SNlx3V5YPi5Lg+MkUxBlTfa7aWHHCUoql1q5Z8BEdDo8oD0nIAjsAwQCL8up2FfK/Fum/CgBb46NAu8NDLSP7kuD/wNUV3u77w8MIuVQ0FCpmtuja+cl+dvyhHgCEQaAhj29X5JpAnF5Xl0CPjxY390QvGZOQIcAY4AR4AjwBEIGgGuy4OGihNyBDgCHAGOAEcgIhHgujwivxYuFEeAI8AR4AhwBIJGgOvyoKHihBwBjgBHgCPAEYhIBLguj8ivhQvFEeAIcAQ4AhyBoBHgujxoqDghR4AjwBHgCHAEIhIBrssj8mvhQmGizzq9hBz+kMF4qHrdk5iwPJgHY6owaV0mx51ydpBgRnMajgBHgCPw6BDguvzRYc9n9oPA5HhX+0ynLRR1PjnlwAQeJK+J6pnp7vuETdul6lRXrnXXFPV7pbbpBpecDsRbp7STfgw/p54Ig8fpZAbDaHE6ad8w7hiTRkyajn9yBDgCHIE5I8BjxcwZOj5wARHwXh6cTEnNWX/vV0FnvyDZMiC2UBvPnOQjh7zMIMPEKunLYh6PhqjknXLo04mWognNC3f3xLGh3SdR9zdIJCmxv8JizNqY9u4anZDyUZufkSj5J0dADwFvfe14qY1Jn6NHxNs4AhICwehy14FYmoIMx5D0YuuvdsD23DiJA//kCIQbgZ7+lnZMlRGXHoMxvcejtOpZdzqiszefyDJpOienhoeict6SVbKmO1C1ux/Vc2FzGtKp0oSQHCFgzlAPl4KNY+xxzwtpJlyFEJu/b1Rw3Jd7noxZox7MaxwBCQGnzfzKxyRrWanUEvCThmfv8cmEFnAcJ4hsBL49k/fbw12ijJgnzSjhKVIE0uU0AaiSoBOrsU2AaUlzfTOfRjYmXLolgwAmEZ9AxUlToEZbXiLpwANlD0OtOTUMqVZRuWJ2DUwlTnKakc16nvll9eZ+pnV8piDOJANCFLO05x5i9tZIgFlEm8FZN22WiQMXpj13UjPLAtNxCo6APgKWWk9X6q6sQf1e3daEpGQAMTuqLgFvXHoIXCt94vzG3psPhV8fzF+evuXXp778+5vP6r6Kf13uOrC3CRU5Sd0tPLn2BxOWA7FON+Yd1+U310b3eXubxf5os5VEggxzxW/ZjEM13D1dwmROy0h77buuT4ru4ebYZPyWaJOPfskqJhkje/GZyffH408+3t2Ap+d6Vu5roKwPSMZ03HmT/FpM7m26e6Y2dksZOuIZz63p6flhelu8KAnp0jHOA6Y214ziVY7AvBCwVk3dr5oXB8PB3npbR15tSYIhAe9YCATcH5z/CBLb5G2E+dU/n2pPOvbplTeP79Cbz58ud5+vOwe7W2VFLo7PLrc728Zge7wev7m2uUdGwDLXwWEaFwkyhOlVligbkoxSpcjpe0QVZL0GqM4dm/VTjxNPtK47qVZpK0zO2gVzd09/n2T3ZhAhOvvuS3GMumU6512c+W5tVgFrP9e3sc97Hs6AI7BICIwP90PeIs3Fp5ERSEzC/fJfW9qP75DApy1yv7bgR5e7qu29sKdsu3YIJO6zH/RpnFdDh73gIrwn/RbPi9WcB0eCDHMWfhkMJInJB6NPWF/WHELTV0N1fvgZtIQ7+jTn1qR3tvf975PfyhJ1MzlrR/WJB+SzvV9A4cnQTspV/mvq3bO6C+eNpaJp/zPzzOMmbRuvcwSWLAJO2+uNUPH7JSv/0hU87/jDn46z4rtH3JCVl8Q2MWXjO2ljY7cAtgaBv7wAACAASURBVCatZ4j1i+7ze1bHbhT/3nEpRORwHdvtVwEYmj1nxxSSq+/QgXubAHqPZotMss+PixQiB9J+oIO0KXwKG9HOLz6YXFwWAAusDBIJqGiISPITQAaZjhcWDAG80FXzPlibraxbODMbucRV88Xa15qzc1KA6FSsSpfHZlr7Oofw9hq20L+qezm1xBqP7XdfIoWQHjynP9xspX+Zm9UjmS7r4RP6ihxHmOAHvXtoal68xhHACAr1ueZ1j4l/5Q5dSBia3IZRPRL0epOY7Kr3aCnQjU7qNetOwQw3r7N1MuM7y6lsxAvvRmWWKKfOFMwQXlxABNpPJh1LbGt/1eh023hf7h29DrDfLJ2UG8iIirDg4u7WiYt0+z5+tnDH6sJjgy3UMw4P110J2dmjl9/ZeCvpyoMJwgqVceqRxnyBAGD7uwMP3sVmdJWv2+C6qD0vJ8fzdgDC9jYAzlVF+RAm9lHxzJ5454285xpwSTZ/JMs+z5zxC5Pae/d/OuDKxbmoDIWNopABZSAj+LNgCNDtOKAGPbkG3b8/0bm+hVPjPttqIcFbwHLSmj453jsZly5t34kFPnM2irqFI4eu2M3EaW5yvBs2k5VBT//n38UHeSEtPC9JD/idPdHSuoSfl4cH12XGBTXl641vXJjqyCEv5mnI32TOP+1ss5mY9/xb+WPDybc8U+TIFJW6JcuWMFVL6RmiBNulKRvWkWEt0ywOqUi7MHVfGEJ0s3oKwrPixiuf3b9EzzdJNb9WliGn+r6nGtk4Tqw7ldjVwc/L1eguWk12Zf/3tp/0T8oFUYx1eTCiErs0KnK7ZIePO9hSfzu2tLqj+EOqNSmPJtguKlGsJlqsW+2OtrFirdoOON1VOypywQsvcd/FB/vEAW7PCGyx5kuKnEyRlH69tdO9T/K0H2ssoYqcEQngprQUCDgvJ1hABGZ7L5PtuGAeN5VZD/s5Z4mJsxRQUWLwrppKJkGR4+a+BTIPC2fVMnFGWtZXGEAmVfF0Uw3VVtSG9Dl6qEVlPj39/vhMhnAkT8/L1esPnNXbMw0xodn/tbLy+tJFgBiuUZHLitlc0tbiXld4zmmrUtyGPkZFfqmUKHJ8TKXnKy5vqq0/kiO1CO2G/x2tfZsocnkKyKm+VZG/SZmCEBBFzswI8PXgOLEt8SdyEHj2UPvNQygO+rE/saXS2I/d2MYexLtcvdoEeyySIhcGoGdc+rkzjAEcdu9U9DpAfPymIDj7kpwDi+JOz3QTvS7s8iWDfCoe8zOP2+m4vuVYOSMDGaKsPxhSXlxsBNaklwXtg+Y/FOvkuOPa06+V6WhHU1nm5qHBT3Tir+m8LWNIZ23ssz/e0SE2bIpZlwzfj5HL5dIT8/jdr5QAsWTZUdWtxImTqPjnCkGgs+1jKP6/6h22dX/li59V13oVCN6wqdS2OWED9AwrR4sKoV7J297c83xqnKqLcPisTTTmU4LT+5WlAy4XOjyM7lcN5ZVHjoD51b83//u1Y38843OQIohmvC83JWwFuOUZB8OwMOPDQ7C1QHugnmhOWohd7/7t2UZgUjs/kFvvEwOJggm9VaF1j/RCShm28yfCEUAN9wm5Qmb8pBhsr4mh/l5WcxrZ31OjPapRxuk92pwH6DQ3h+fu5a6aO0+/dnIdGXutr0YlnuGROZ1o5i4KodgPos2A8WijyZV38pprC5uzoiZn5yASH7L0EfCM3oaMndrfJFNimv9tcVzyi3B5xAtWUxAQjA/fgK9vWNa9raUtFhsIwYZjwbDScuD1R4VA3r/+Dv7a5Pj2kN4Vc2NdTjfQt/wJfef2TQDB7OmPbGH78Oxcbedf2Ok494VFwEhbA7muBnoXyeQVgBhjFTlIRntZVGK9lyswe3dIqWBJ4oCR5qzW7q6aImE9gbbxzRh9w7Jzytk6BUMQfSJL8bGncd9UXNgKDTaXrChy0md6YW1X9+yvJlxdsehet4Y08dBvBIUV+LjdXwPsXPgX52HgFh7jxZ3h2Y1Z8NHQtwDP+s7rx8a+fsMWIAfPPoMwpgr1RScEvg89wE7Qrjl96cLTQi7O7f9UPrDXYYrH5zA05vsWOqS86ZEiEPXM2jnMH1WwGZ3bFdt4UAFfVfOQC2/Edx3jxE13NwBq9MPUYR5gjSljDeDReyZMQ6w2YquKh1D5ufc49aW3YTD5p+PVuhwy4pOvuTwvWBfVEU9HSN70yBFITHxeRwavux+0VnEVGd1JJ5lUbYYVson3+xCC27jL50+EIuD+4PVVT7yutqh/OyDFc/UV2o8ujzt4aDfcPFXdoRnlqm5NoL5mhOC6vYG934U+522tvVsLcualy8caz2on1cgQQpV4291EbzvNENdZ+eabpkeohlUG3Rl4Y5gQWJN+UkdBajObqSaLelKjaGmvt64bTfHD3XiwjTyf8kjZzGgg2KdMKg66lcdRksO1qTknrId1lhRr0l+K7asKJfOb7iS8cckjYC4pf6On4k+dqhfxdFy+kbFzh0lp7B9VXUJz/K0RXsm3Kv1+S6a8ooyvmztUHABGaxuc4jB9AnA0+F5sYydiOLDNvBx2BEaGvtHybP+fjwB+V7BN207rfnQ5QK69dQ+c28veCMfLY86d0o0yStBUoFz1JpfHjsKxBm2oON2p2Ubc4vdeco7TpvGzR0aTGVc1ls6nTGwDrKsdnp1Lvm+Sto4vPrEHL6+zd8rJWyRrhZyzDD5C8YZHhgC9iU4vmpPsJrpiTP6MulrnEYPMZL1cIDjQRWeWRE02jHmBbtaVhGk6Q1VN6GYv3Zdj2jHYXFdvTNprJYBX4Z08cDYDzUosWmovFH/8er7s6UbupFXC6dMqZ7cblTaZAK+cFX72vMpVLQBuCbbTlVCZxdxKx6vkNsiVnd0SbLZivDjO3inH62f/laCSASdBK8KNtnbB4crTYBtMkDkEkIB3zwuBHQfLt8E3A/JqDP3Yi/4KJWeN0qsYn5dTMcj97+321dkbj4pSsTfQSBMSDOJt79hTQr+ShQXr5OZ3E34WxDbh3W5yJUxqgeyNt4UWYRjEHfzTsUvZO1bbsZ7+nku4rc7QY/PejefwvxhTVuWCjrfgrkDhDlkAnOhBWWN29qnUWMKHjBCETEIhNwpVHyZCs4EM0hj+udAIzHx3DyB0M7vk7EbFozfRtYKSoK2d7Bl5SqpVsy9HJlUkH5qJGRtVYM6BNVDnwrCyFg09QxaoKNwvx8N4kusFhHi0+gHsAnHi/csIAby97cT73JJvWkblLY9aiSZX3rqUdwXjwIgLv+IWT1uwm3IBJ+KXnmgzZz1WKdR9js+JDMkow2MSruw1OakNzCW1p9uyNpkrsOXFii7hQrzcywsLhgA6rv8UV/rEllXSDJgn7e9SPFepTflc9fDhQ7n2z39gRBb+cAQWEIE/dB0x4i75oBn1s67pDM3ktPP97ultTDoWppMtyvwZF3ehHzOr/pypYxUnbnEO2KyccLPrBrWbHmGOSdXUl+KEGfEsX+EgTChnZtMJF8+KzMvLBIG3/qVlgd+E7Ozd5erL4gs85eKwT/hF/+JMtIRn+WUmCh9gX76EX4+LvvQQWCtuXnUkn/b2RJt0bNfoDR6NweB0Rvg0oY9b4TPTpgzBhC534679B/NJep9NbqMFGkVOrYZj4l5ujgPiVI9DAt+MJzPCeJQq1QplLWZmU8/HaxyB4BGgCcuTpUgv3vp9aKJ3cut38AAuO0q+L192X2lkv5CffXlkC86l4wjMHYEF2JfTI3a82yY8aIGvDmpFKw1YMp98Xx74q+L78sAYcQqOAEeAIxCJCGDY1/slkSgYl+nRIODXj/3RiMRn5QhwBDgCHAGOAEcgBAS4Lg8BLE7KEeAIcAQ4AhyBCESA6/II/FK4SBwBjgBHgCPAEQgBAa7LQwCLk3IEOAIcAY4ARyACEeC6PAK/FC4SR4AjwBHgCHAEQkCA6/IQwOKkHAGOAEeAI8ARiEAEuC6PwC9lxYo0OzNp9O6zva36YdSNBvhp9/bMgxWGbJNyrviZImxdGGnu+LiQhDVsPAMzmvX2GORWD1YejMDj+LxVj0lPv357YKk4BUeAI2CIAI/7ZggN71h0BGa7ba4+41k7JzI1QVKNaQ17MJpbSztA0MFTRXo1v8kGl/MZq0UTh46N8Kqm1635hJLVpQIYGvyk7vH5vzjLXXgpYwFmPVXdLeogtcrwYOShGdyj1095Ic6kjMTSbO8XE5ND93ozaXR6VRevcAQ4AnNHgOvyuWPHRy4EAvoKhqjJe1nqaOfy7LrqVu7VFJD/4TKAnvHeyWiS7IQ+cqh2DTFWRXrfDv0WTO6iStBiGKd9wqxdCugzxNaonOCztIWynuj7YjwzQy8Sbc8PfUaKnAgZW2jwRchv4L08CJiQRgpeS78gml1mcqxzfebhk0oYXYr8Wg1oMp+VXPDW146X2nJWMgT83UNBwFCXuzH7mb3XhxVJPnYw3qf5UTbQRKs3BQkegXiYZbVAzMfGomAgiZApbk/9g3ezWWrM+07Sxfp7i4AEEj/DKSSCyP1c82QK3A1dPFMZVc/qgQGUREZcOkOPgdMPFzB1UpztrZuKL9OqOqqWtApbM/JRVsWI8cYikLQu94zj3tMX/2Ji80vWKF0eMY9HA6az8/tMjne1xyaX9NUUiYcDdD2Edvvp3i+g8KUfaoq65fGYeOZwMx6gzJokxS93rdyC02Z+5WOANy6UBo0BDc/e45MJLejxnDBCEbhyaEt+gyRbVvlI+6uJUk3zaajLE/ddfGAhyUM3sclJUU9o05VqGC5+FdOeDhwk02JW8rrFn56khSVZVh27mFUOUfDZe4BpYQXbmrSerdJywLcISKBiqTeFiiBiK300H6ieeLF6jQZtPf2fNMDmvHstRV1+9RYzHJXcV08xpuw16Tvh8yJH9AnGlk60FMSUPBWF5/rSnp5hMd+i7ibel2mAZYrvAKlF5N+cJjXofaJ5HFKtmuMDPUJsUzbcChqzve9/n0wTvKYXkERznzSsNRNua6K+G/vxrTRTDBwWBKCriuTMNdiFlPxRELDUerpSd2UNKi0BSwlJyWhsCkjGCZYSAtdKnzh469SXD396lkr97Zm83yblgZE6N9Tl+q+cax+0j6SeaSzPLTZaHegPXGGt29+t33+x9JJz/OA+TKvFPLn2BxN2pr4AxUWYYgGklln6sbHLNAEK0tYzvrurL8+cHoNpzbqBVcm64zPSXvuuq6boB8XeGxOXlTfYUtVvlszmM93fT6LxuSBaf8+qyzbUxvbuGjzOVz+TNkenugXyng5+PeFtxVxtcTN1jq7Y7MNlqDv9Pd7L3ye/lSW8INHE19Zuhom+IdWQvqIJoS5tuGchRmQ709p396UsSwz6vo396mSaKfPpnGeEI/PpMYiXDzUAv6Mv1uaUrI1XFgGqKXglRASsVVP3q0IcEyS5t97WkVdbkhAkOScLDwLuD85/hBvxNwVFjjyfPVRX3pRefbr91Xq9LOYh6nKARHMS3HS0jRVHmKU9PPCFj8v6DVvgVvjYcU7BIiApclQbooUXoi3NVtxB1uBPnb9jYIgqMG9u6G6pU3bnphdi4c5aUXNPjjsa8GRXa3UPVrAg6TROeeT8W9zmygzo9vrxqKC1YBR8/0nRIOrdlwPuttHwAOaXRc7T3Q0zMXlPm3daLcpcNN+rtLihIq1RctH29JN07+IsEy2o8lNSc9ZT5CE2p+SebHUX3uXuS1b2pEN+QV6IKATGh/tBT3lElJDLT5iRoW+gK3EEda78bubf7M6qbhr5FvJkBS/38fzlChThLd25fRM2HVJvysM7wzLkNnt3COZjY5cOs7NMPuCIZ+qo6YscxHtLpY1k6mhzHkzHyjvX2ZmYtMMnxV6yYa3V4SwPpgWqwNRNADpb7cX+cUxJNcfgDTH51bQi0vq0Ex0MSYJ28njruvs0Cwuhw89/M9IkRU6IJBNLXDo6G9JHsaXTfXlmwLWFOI5/PDoEnLbXG6Hi949OgJU6c1LKcwB/bWk/viO4hVTI+3K3ZwS2WPNl97cxcqZ+XYab8eqSvOd2t07YQXEQI9XtMr1QENy1hDLDAZj2/fTYXuIJsOXYYEsodn6GFbBTSJIonIG4reV7XZCbrSyIAAISSJzwE/3USm/Zr7hylTbGRc7ALU6hnWPJ3xTi6xPwExWvRh1JGCaw/9N62Ft6jgKyaGYYSQGoQZgcd3avM6nb1DViSJ8mjlR+1VVG2uHatZ/bBluKQFedo8pnZxl739E5JDhg93teQNOxek6dmnaVIB5Rqx2/aaPO4AVtipLM4NIsaAZ3dQ4pAs+0evpgRrafUxuG4nAujWI+hcsF+qsiQma8LKNMUlIZXiux6K3PtVTcEN/cIAE5Q/NiRVeHjqFb8HqjXDIqb10qNaugFN3oaJvuFMxw4m03VZsjje8sfwy1uPBUZj1WSUs6U0j0/DO8CCS+eeHhm2qWnv9t6npud53OphzpQtPlgj7b/+lFUckRDTHynmvAJal21ATZ56+46CGx5D3nqCrcuOnQwIN3iViEQ6ydVeeUZ1LrxICg4JHD6kJJT4tHv8SF+zYAdlUlXXkwEUeHjLpZ44P6lTU1f1NQUkIwUvZgIpvWcLqNqTdR7Sm6PCABQO/R7I1HpYlx5cEqcmwmLnKIAFn64L5wQR5/UyCSroTs7NHL72y8RTFECchLHWnMZ5ZERJFDvYgDEbV006dX3juz47YXQPqKF0R0kSkxhuvzj4mzCO5R9M4V3nd6uUDR2UQ1Euu31aIePDMhWdnZduLp/bizyPPjJKAfVsAnpmQzNdfHqxQ5Y8kPyCE0gmDPyzVcBcWsaWSrM2hmZ+vkqlut9TA6ExwfjzpJDg4YZ35qSKeNZIjqnhvq/qc8RY4WbMcDi+anuuumTeqVijyL/rJM6Cb7cplwJRaopkTd2UF1p6chf5M5/7SzzWZiwPhb+WPDybc8U0Q9E6WeZUtgdK1ImGC7NGXDMjKsZcaKQyrSLkzdF9QzEmimEBYKr3x2/xL9P4dU82tlGXKq73uqkY3jxLpTibrLCPV0vLbQCFw5Ww2nvjykXq3JkwbW5ef2bjwnkwNquIvyrlq7R0flmpR+vbXTvY/dMfeiIv9Q2qEm7jv5XuuOgncs4qWsscYSOypyZae+/d0r7xXuqO4oloeIk1+1oyJXVgn7FJkClAJPMd7W2rv/kKDIkRnxGM8/b28bg+2iAgtIgKNUe1yyItmrZ4GIj98UQNx5d/ubogm2K4uMRIt1q51xfRhrrLpIVjCiBPHFDXZH6tU7D1oG5i2TMQOiFCeMuw16Glyfg6TO6RmtwXbc6GY2LhqUHSe1zBtMhM1DrpoGiEmJmhxiVgZofG6O760bn/G5tGbMKLgejVnb8Lxcw21N+knDs2ey1rn29GuyblYNjbYwt72FHm+d50n2NMHnnpuJWTZZylTs2Arfl7NoqMrEcM1ugs0lbS3udYXnnLYqZUH6MSpyeZ9tKj1fcXlTbf2RHM3OW8WXqYzWvk0UubLPzqm+VZG/SZmCENxARc7MCPD14DiAiWHDi5GCQPvJfDj7UHGF08oVWJcLxm3tOFonO29Bp7IW7C2ardXunZIip4Pi8gvSj7aOuYHset1Ox/UtViwwT1xyChy96vowV1IqtO8cWB5ofMKZMX6KQUyhM2PiPju95yYwDkignZ+sSIaYJYu2/1HV1d+FP62/WBKi0Vu+IoV6632wSirHrwZixGPOaHEI8b2StuymMvXxtt6eHhnpXk8njtZV92JSZmBbthWmZgo08cuIAOll4tGyKM3kvUmxtIQ/EEPf0wS8Gm7KUFY/8uvNTM76mO7lTvm8XGlRSit7X97Z9jEUt8jWbAqLdX/li5bq2v0WeWv+hk2lts0JG6BnGM2RBvsyBVxS8rY39zxfpP4HSjhUtjmqLOQnmhKcPq0sHcBU2uEpVXPhtQhBoP3kqtZ/fXhmmx9xAutyP4Oxi1hlL9LT64kBopvRbNvqfwTtvSlayN0jvXCzNzX2lHbMHm3D/u0q1a7tNq4HMwVap1txJ00vMG/Fc26fRUNAAp/56ZLFXne2LHvRTpp9ZAixIb74xJ6NiskEXNX23v2fzhH2EOem5KgLhyb8xivFeCNgylCs676zYHRV3EbrPynU9XtidgbvOutTyK3TzqqJmJLM5Gvdw2h8zoTuIIOZpNzrEuzPMidSiADfN5U8hhVhMWTFu3lVshGCuAvEf+epqVICvLDjMdiLvHhi27HM9+UaQKSqZ/Q2ZOxUb2FwTZmY5n9bHJf8Ilwe8YLKpUPiqf0cH74BX9+wrHtb21EsNhCCDcdM2m5ejzwEUJH/v/iRdn+KHIWely5HzV1ArLKKhXwuMITqxTaHOYKYQjxsJgaGHavt4KvRAxJo5CKX9wAvFCylZ3vZsa3ZpatxcUYfPyaZhXgr71cT2jDpGPpbe8qLoVoMlQeRyt+tM3rR/IWAt8Px7Jm6cBes6b1GXzQmzjzR1TsZIIT4zHf3YL35ZbXJmlgXJsxMCBrCkDZSzr7/meN5uS+jObSQS2iT4HL4uBASxa4x/mML2VuvtUpWEJ/5qM8gaO7UkXN9vIqGF9BnYgKuqHxYLpMGt/trgJ0L/zI8DNzCY7zgM3j+8muiyA3DvckCzEeXC/s25fhcZuq/QDbKkl0dz9dhyD/5fHtDm0LwtiNuXzsOmJVjfkWIgAQSKXEmWGKP60D26AnJCXHRZZ/2tKPCUE+rp5hneqaD2Fir+YRSw2gnnZD6GvHnwktc4mMqM3uK+r3GPttIh652Mcp9NmlkqJ8alal7Xo553ojFe7q3FSOm+bNShDg58T1kbK4hjlaR4/l9Fmkgpw5rf+XrY6gochLANb1Ax4Cv4resKomJzwPaezSP190PWqu4iiSknTTZxKPDsPET0i7fmA3vWVAErpWmj1X8dFxrw9GbcxFyno4Mj7Ezuy5fhK0FOYJwxP2KRJ5hCYiz99kOdcs8aoGnGGs8cB4dPpiHWJvhlkdqDEjADJWKxF0OICk5XmpYAp8Y36apIBbPGvAqQSOeyy3mQy5EoeZ+4Yeaumn/80ZlBNxY+2fgr5fsmPXdxKItJ6AF1bnhaFyLRNGIpIYUATuIJ7mBTzg7dkERYCci5RBSlOKe24HX91V/tsFJwDv3ciPegiO2d4bG1dnQHfBL10q1tOvmkvI3eir+1Kl6C0/H5RsZO3eYlMb+0VGlgv7kf2uEV/LJUXcwjymvKOPr5g4VB4DR2ganOFqfABwN9R5//BkO/sh4XxgQwKCt5zf2Ht+hYnWt9JBgLVS1YmU++3IS2uwoE89VOjvHf22us+fXS+FL8bKWPVm0w5OL1+fQ4i0fSAvO0mzoctwTH4GGFq2gc68HnCI+Huw7soE5Jice3em7XHHipAEJfIS7+g5JlLL/0/mdPviwXeAGcsZ/qaBsV2vp0ZunJCcGlX/+ggmA1l3IqY3DA+3DgOFcuvE6U+aCTWbE2FiR0xEZaYV5jpYig8QkmFssz3zYdwNqNFk42u9OBAz/MrdpyGFEnzCU2AnWzATjBINh1X186ckdAWAz1Uo2dk2UmEl0YoCouQm7FEdZai8UP/Z6fqp0AYzcSauE006Vs9uNSlttrnRLrbO88DO0mQdvNkmwna5stmTlgnydDK+S2+B0m4RXgs1W/PbrqntueP3sv/5tqlaiED7RinCjrd1TQmTzNNgGE2QOajpeCy8C7g/+OPAfF+oRdebBwK63Uv6TaVCKhrqceLHRPGnX8U6aXnAVenfrChTukH70UXUJWUZQDRAFIE2CB+qWy7F4cZk+PkfXxBneTFK2iJezWQLWPV68Gud7PE9un0sZxgAEPiwTvCnnZwoq1Ab7lXI4vjpWzgunVWB+CMQVDMB1+RUITy0HmYx0CpQqVAO+RUACyQ+RTOAzhYRkQWyTeAoutaAwt6U4PCVwkvj97RNy1RA25J9BneugNqsb6QrjgyHGMF66GKybeLbH4w7vEzx8SXk65Fn0jthZJpvZClNGrUPClZ/0Z7JGj/dCcLSQ6OhKiBWBh/erezk7/eYsYebSv/jOEARbbP/BWxZtCkRNpwuMJFnKNJCTcRoXLxzG9p7+lnYEKgj7uWJyD/Qyy6Ufb2878T635JuGMVg8KkUOyRj4Je/KrnWPiRlTMNJLW7CbcgEk4peeaDNLYV7A5/icyJCMMjwmgcpek5PawFxSe7ota5O5AltIvJocuYcXFhCBb//7y28+6trykc8Uv2t+1qeNNKx6+PCh3PHPf/g9YJHpgi+Qg2c8gl1a29PgX285UZK1wm0mEoD4bqjyr0rBAMLxun/oOqJhgyqk+5ks3WTe/m5+a86VKdMAG2ugvm++SVboXTVVPjRRRLKJHN7m42on34mXj/NpWDo5XTf7goxIhBtamIUngAefwoJukeWJlHaRmyEf+lLktFp49OCS+uRPjBvzc6Z0J1BuxQKj5tlmf56G5Lu7k+pzqd1gX67muvxqb/1LGC2NuvCQnb27XH1ZXJdwqTUm/KJ/qYm86PL+khgxDffliy4On/DRIkAv0e3ds4HN04pLsb24j7cvnGTCbpgN4s3OpXPzW88XjB0SoJyS6hMDfNqJl9qbDdJ1o/OWrx2bvRNP5pvtvQyZBvnHmGBqkgkalwJfPWV0j8tHfvRHy37yuMvXn984qjzlIQV4EXVqEMfwgElo1E74rDA6i4aefidoMs0o6xU8JTlsEEZm+rtZ8HuxkJ2Xl/URoLFXk6VIL976fcREH7wFXp8pb13CCCzgvly20iM8vle8ljBmy1h0Ykdhouv7nBTM/9V99+Xz58k5cAQiHIEF2JfTI3a82yY8urHWpc4l/cn35YG/ProvX0BdHlgCTrHyEOC6fOV95/yNYQF0+UpBlevywN801eWLcCctsCScgiPAEeAIcAQ4AhyBOSPAdfmcU6hidAAAIABJREFUoeMDOQIcAY4AR4AjEBEIcF0eEV8DF4IjwBHgCHAEOAJzRoDr8jlDxwdyBDgCHAGOAEcgIhDgujwivgYuBEeAI8AR4AhwBOaMANflc4aOD+QIcAQ4AhwBjkBEIMB1eUR8DVwIAQGML/Z5q5KdTAULxlYz6lLoMEqaw5cDxktxipEwFdJApWlnoCwvgTjI/SiVn6QsMhkvcAQ4AhyBOSLAdfkcgePDFgCB2bFrM5MNLn29G/M4NLhqAijFNU+mREUDSdTBPLM/3iG5ufTZMnTqYrQFk7bprQzUZEHWMFcYqnMMi+ZYYTnBgsSHk3EEOALzQoDr8nnBF/xg9/nGq8FTB0nZ0Xh2LEjSpUA2OTU8BBj7Uzc2u/ACm0+kmQK9ypOZQlJUTO9Nt/hBsNVnmZH2WknUZENfrxLWXJ8wmFYqOQnjWgjdNUVdYeEZzLycZoki4K2vVSdFXaLvwcVeJAQMdTlGYCV5rLV/e5aV8gBMKELf8R2XGm/XgTCm8cb0JLEbU+2aTMLqCUOsid/OXkeI4yKafKb7+0mdeOkBZEazvCoZ9tBMp01Ij93d2eAi9vbJe5MQa9Yk2QzAVeyOKjBvTnk6XieT6dwt8KadqTr8gpOHU60IBJw287rHLBWDIbwshmdf95g5v9YbwhhOuqQQaD+5Ku8vbmORDXOrkDyhFhKdexNNiClyQLUkpcg05rmEeuIOtgwkv7Ox4KLz6rvZ22XBO5znsKzKSSr3hV7ItT9wJWC+uNBHGo6Qvp3lpMuJgX3zS1mhJrFmkpcgXGjE7oO3ssT0qZjgCxNjd9+DPLNJhSVu2dekFwjpTWkiMlWvttJXpP+r2qfKzK0dxdTR8g935TpmPamFzzFxalC5y+RhvLBiELDUerpSd2Xp/6vTRyEhKRkgZJ8QfV68NQIRuFZa9FfIKvcjmaEu1x+Tax+0j6SeaSzPLU7Up4jcVvd5e5vFfjBeV8KR4THYLnW5PSO6RAEb/U4RcHSIBPHxm0IcEdHkPWOdkPpaULtn1L6eJ2sVhe3nvaJioPfaDAx117RrqYZBSGaKich0c3WTlF86CU9ZNj3TXgATaQm8IIAqRx+hZJ727ppgVwPMKF7kCOggYK2aul+l0x6GJm+9rSOvtiQhDKw4i7khcOXQeSh5Dgb8jQ5RlwMkmpPgpqNtrNhAKfqb7NH2uUdGQDcnYNLu/VuabnsBRF0+3taatH9PL9mah/gYThEinxVI7v1qAoaguyfOkuFXLxKNGFvYvPnH4/3ek+LZuTa7Nm55BQRx47vz52FcIjSzqTnFvXs8GDjMB49+RrRJJDZaEIjdYmpX0RIQ/ASckiPw6BEYH+6HvEcvxsqVoP1kZcp//hn++FF4dflSBbTDXnAR3tNPqJywIQWOXnV9mJtN3+7O7RTLTmgKWZf7m2KpwrZIck+Od5F9M9rXUb8a6UWi40HyjDOdTJNlU5vZ5Wa0OU73dn8fTez2wvoAFwFppp4pyQgv2NgZep9i9DOBaXwG6Tfo5EHXJ+StHIFIQsBpe70RKn4fSSKtLFmulf6/+D+3PwsfBHhtQ983o3HE/rzFmi+ZowEzXrP+cSonMsmDLHZj9vlxwpB6gQn+dAc6RL+zAx0SWWGjW+am4qMauJrtEhna0UWccdZTOehdfYd6t+1tAug9mi1684nySC+ZnJQOQ2OiW0GH81bSeqmH+WSEV8kAEMwUAiMjIX16N2qm8CFQvSMj5VIszva+PxhdQjzCnowJj+7EnTq5gZaxBiaezpTs9oIP/EzMOvk03S9Ys3eHop7UeqnhmiAUF3TpTnxUbKhuAH5F453LAgFvfS76uIl/5fq+LwxNboOuy43g9Ub57Kr3aIGhbnT+pmCGm9fZRIMW5dJZTmV75WOAG5VZopw6U2in5PVwIuD+4H821r0azIl2aDZ2VEWp9t79n14UWaN62zvynmvAJal21GrZ56+49sXRt8n+cOLKhsIdR1PqxRbBC+wINLTQ4/bcK4C9e52tEwMfohbPPpV65NjgxEAiKSvOaHTSJKQRfNOI4iw8NihyEN3KLr+z8VbSlQcTZF5Cf6QxXyAA2P7uwIN3sRlXDHUbXBd1jwYSLdat9lHU5fhebg/sssTBiOr78CdDcFMgOz9CYi++V8HF3a0TF+lr4kJnh/KaVBZfgnOQ/p5KzKVZwZPy9ZmHM3/+vEGRf6a1fywzLTili/vv/pqqCWUwQExJdtZXXb3fPQ07Fev69HezqN2jglwuTP48jcfg7zs6h1jGtPz+ePxJhS1tIofrOpSkb3AaD+afWQvXfp4B4bIcHcH/s7IRQE35euMbF6Y6cggOnob8Teb80842m4mB5W/ljw0n3/JMmbENlboly5YwVUvpGaIE26UpG9aRYS3TLA6pSLswdV8YQnSzegrCs+LGK5/dv0QPH0k1v1aWIaf6vqca2ThOrDuV2NXBz8vV6C5KzfOX07C3nnz/gZ/Auvzc3o2MtVlWNoS1do+OujAp/Xprp3uf7BkXd/DQ7qNncMubLah/t9Ox6ZC0FKDi7f/UTrQX8eRK3/UneSDtw/+MNZbYUZFTGtq2/d0r7xXuqO4o/jBXooEm2D7gkqpUMYd4ok9mdwjub+4RSN4nc6aFoGRQD9Gp+RWS2OcRW/k10cG+/nZsqfKaY41VF9Pfc2kI6nTmWWJN084v1r52Mhomf2YFjyqIh+MO50v+7por9Blph5sVk7vUnmWSSsh8GmKjYWoGNDpYptAW6AW5p187qfarJ4sGKNQqchxLLo6nSzw0p+PoSw+wNmbo3gw9RZCo+OcKRoAYrlGRy4rZXNLW4l5XeM5pq1J8ej5GRX6pVPwhN5Wer7i8qbb+SI7UEgC+0dq3iSKXp4Cc6lsV+ZuUKQgBUeTMjABfD46L7pwB2PPuhUfg2zNlY4XtrwY5UWAb+/5PBx5MyH+yLiH8yc0oYfsr2Z9x166dONeyn/jKCc3ErWynpHS1lHp11P3XtyQI6wCpPy45Bc5ddUlV/Nyt4jkXB+/1G7b0XnLiP2PXZbAIBgCZf3AyyORGBX9CXr3aBHs082aX29PPnWkULP9UBuZow2iSpdburfvBrKMa8TXWpL8UG3qwNub9J8c/l6O24RXzlLX4o4jaNLiHXJCL2bZOYxmf+e4epKzVNAZkSCwBMY9Hw4SH3xkKCNbKIOhs+xiK/696h23dX/niZ9W1XgWBN2wqtW1O2AA9w34uGCsjseRtb+55PjVO1UY4fNYmGvMpwen9ytIBTKUdHkb3q4byyqIj4P7gjwP/cXxH0PMG3pf7Z0UNvwBbqG1csG+3akagTqpLrXMdfDcbxjovpVhYJawh9a26R3rhZm9q7Clt1x5tw/zqcfkF6UdH7sDYGCSp/xfDl1pwGcYx3tnWAu0hPb0yIFn+UYaUMvWaZn5vHBGjp+GFNJORJBnxOSkTnV9NWzKijUiwXevELpOmoPu6FVrHvbgXRx28/inTMz/gIbpJOj6XCXUKJFRcVPJb2sP7mYkZWG8OVZdT/tHmPGgJ9C46kvCmZYiAZ/Q2ZOzU/t9sSkzzvy2OS34RLo94wWoKApPx4Rvw9Q3Lure1tMViAyHYcCwYVloOvL4YCFw7PbSv/s0QZpqXLsdTZLVlWH9iavQm59+JztFdZdI/JX1avVZcKEiH33rd4WkjirN17KrTAWY9CRdWhju3bwIUhOdFlhSXaL+adU38tqi7z/hT5PiyghP7TOv4TEGcySdWDBSgUX0Wr5hvfikaYn6efn98JiOwmZ0Y2PPML8eEE0vTC7FQ9YO3TL7GFk7mnNeSQsDt/hpg58KL/Lz2AH7hp+QzhAuBbfUFJ1c9cVDN7pukJ9CF4d/bftLZrwe2sat5sTVXNfGDU1nd2W6lHJ+za0vT5Y7xtpEExQFe6fZXwgN4f91h7DMlbL15qsCucwSw8DKghV/nTag7gni+QGSQPe11aJdnE+ppP7HZmXdGk/hgi+Jhjp5ojprj46JFnWyyaQzXmHXJ8P1YwODqk+OOBsjZ6buGIDlaYmK1m3VGDL/FjKc2w0QXm+ptcryXW939YrZMOxMTn9d5M6+7H7RWcRUZ3UknmVRthhWyiff7EILbuMvnT6QikHf84U835b+RU89h3LcR0qKjyPEd5qPLg4eAeMCdO3P8dlKO1q4UiAfZ0yvH7RL1WOPZDqk8t09fDmTBgYcFmrN5wn2OMvhOYSgqwee6vQFv1jEP+hb0bi0QEZPt7QqBEGhWqa/gEnrCD+HdcTkSHHqiZefA4Cc0aan38iCUxJsIPHgGv7YTt+akbPTQC3InZFYsGd5Sg9BunNMzeynZWnSmJlPL5L27LHteXikImEvK3+ip+FOn6n09HZdvZOzcYVIa+0dHlQr6k/+tEV7Jt7JNfsqmvKKMr5s7VBwARmsbnOIgfQJwNPhebGOnYTiwzbz8yBGYjy4nu0nZOQvfBM/OJd8311nhQrn8fsQDDjbgXa9Qn/jiBjsczWauU+ONtSOQH4oDHZ1T9m7D2vjZI6PJWg7EpU7WnSoxg5Uh4BQqrqpKrr11T1MB3rAXW8mdtKNwrEG83QegIUAQzoxspTfmD8xzWaOSYylWZnu/mPDJn0Zc58jLkCg0sVlywDXcHA8NOtjNsfqNvXUkbqu+MaDnhz7wvXGuHo+1nv6WdsDMrSTjy/tgbbYebra+TAWIKtick4KpX/q9dBDxpOPPykTAUnuh+OPXlVQo5E5aJZw+rXJ2u1FpU1zhOssLP3te5aoWALkE2+lKqMxibqXjVXIb5MrObgk2WzFeHGfvlOP1s/9KUMmAk6AV4UZbu3Bz3dNgG0yQOQSQgHeHG4GuMfVdaRV/w/NyeqOaOKVfxztp+llG8N4UuSAuO6YRj/cyek08Fm9PXVTNA+s37NG4YVN1hefEe/dscJ2EI6Xk5lu2PXnCcjn71HU8Po4F4Y4W8ZY3k5wuRwWO7NE1ueDehM0FsU04O7mlJrWANgdM3ME/HbuUvWO1HcmJeNRZXZKhFciRfFL6JjOuNqRGKF09JB7V+5MB+YmP7hSKSP6FxHvwg3gzXvLy22q/8kBW5JQ/ErTiakkgICCUVb8DLvQoXOFPz9hd5eoa2To/KQCCF9VIONjvk2uZy2kYVO5EbE2Vy/mMzm03zV0yEmW9DixlorGdRJmF2F/pHaLjQNTf4oOBY3UuyAmdaDDIvFvU3VKEiw+rGT3pnpFG8c8VhgDe3nbifW7JNy2j8pZHrUSTK29dyruC2c/Ec5jiFk9bsJtyAUvil55oM2c9VinUfY7PiQzJKMNjEvbsNTmpDcwltafbsjaZK7DlxYou4UK83MsLC47AlUNb8huEWb7Jf+KvRuflqx4+fCjL8s9/3JbLYS502LM9JVIMmTDz5uyWEAJ/6DqiLy2ao21E72rjw6jiwETlaAkkdauQSTSE4b0sDNrqM5+oetHRXbkOR4K9MGsCaYzClrRgCBphhy11y59ijHet8HK/qqAEljFmqBrAK0sdgbf+pWWBX4Hs7N3l6sviCzzl4rBP+EX/4ky0hGf5ZSYKv8C6XAwMdzG5zg7vBuElt4Th5KIHhUDIulzkSgOqB0oSKkWLo8oSU6ooqloj26yXXE6TvNiIwr7ns0pQhgjX3nAnrW97VwhDKtE3Uq0nQhrOiZcSAuHX5Wgwz3o7WYr0QkK2XS6SQ7YtJWgCycp1eSCEABZLlxMbuGgADywUp1jmCBjq8mX+3vz1VjQC4dflNOxrJd5tEx60wFeHZoGXRkb6J9flgb8hqssNz8sDjw+GAgOwT9iDIeQ0HAGOAEeAIxA0Ahj29X5J0NSccNkjMB8/9mUPDn9BjgBHgCPAEeAILAEEuC5fAl8SF5EjwBHgCHAEOAJ+EOC63A84vIsjwBHgCHAEOAJLAAGuy5fAl8RF5AhwBDgCHAGOgB8EuC73Aw7v4ghwBDgCHAGOwBJAgOvyJfAlcRE5AhwBjgBHgCPgBwGuy/2Aw7sWGQEMn+LAFOOR8GBsOFkSjBXzuXEI90iQlsvAEeAIrHAEFvh++QpHl79+SAiQ5CUAVf1mvcCrIqfJaS9Em/SColMCXA14ntTGeRWH0vBt4Ce+m0gnffR9MZ4pJTufbOjrzfSJLytRip8kauzgpKbRuBruQHLGM/EejgBHYLkjsAR0uft8o3tfMU2F8si+jUiQ4ZG9/CJNTNKdgf8oraKylCKu6wuGicgcnfpdACmxvzLq8mmP2bYuSm7MM4ux1qkM0YbxXDH1qioCPFlATJgPSwlaBH5CY1gjwsqC8gJHgCOwEhEw0uVSrjBthjTXgVia0IxNVrZwuIlJz3a37vMzhyQqISEJ0A7G+yH20yW9GpKQt15/tQO258ZJidf8y+CHLe8KCoGZ1j6Sg/ylH2qKug0HYPTyZmsUzPa2TkOBmL7Mh9hQ0xMNem1tlP6eXsl3wjB01YjpibCtu0bIhJaCDKKmv5uGDCMBGAa8yBEIhAAJpV5xQ6DCbGmXxGxpNAuqGKKVZCcrSQjEaMH6O8sfe71R4O5XEqfNXJ26LGPC+4fW/cHrp5Mu1OdpqZj8ZkrX75pvMpTfnsn77eEuqbfk7MMz26QK+QyCg0JupMsxn+lA8jsbCy46r76breyJO5wkM6lWwSvswlzCELCuhOzsUb9siagHCQUq4zq/lMaddNGgpBnFamwT4HoltzgxKBmMOfOeYBCYHHc0oPU7DY3nxglDZUZr0mk6cLkejsKa9LdS73avs0ic2fyn6r01NeNnckUeDtRXPA8xJ8pUhwmhIOlSNp1IFHKdySFaMaf4qUeHE85eOIz5WKfMKAORNsuWMFWboyOQ48QrH8Pzp3V6lmkTqvCkY98IL/e7Zr2XTCkf+enVRL0e2nat9ImDt059+bD9WVolen1VXvlIOzMkAAcVayNdLhONDI/Bdmmn6/aMyB3hLbjP29ss9rluqecpi+vA3iZU5EpKVhJG3nIg1ukGMP4m5jkpHy4jMNv7/iBardMn+2suP6UxR8tEQI3bYJh1VCGcY2nyXl/DYJ+yEUc2evtywj1K77dsjtPyYSsYAce5CqjospkeKQTeeltHXq3uvr+znChyyVQA48PEfjA6Cjk+RoLO8lNQ/CIsWNLsR4qQ/uSJb154+CaA5y+/Tq/Wo/h2GOJ26HUIbe4Pzn+UVT7ypqDIse3ZQ+1nB544eLr9VWnjHoCDhrdfXZ60e/+WptteAFGXj7e1Ju3f00u25uF+3CMjYAk30+D4uc/XnQM0ocepybPL7c42Zh2j7uW1sCHgreuDt6yWGJhpvQftE6Ip25c9NW5Hw+wMrFGOsX3JwP95+dM6I2iT9yv2tJ6Y3OV05up9ucBg2nn850zDnKpGk/B2jgCDwOjIMKT9m6wXE2yXpmxM9yIVx4f7wcc+TOYera1tfDG/i+zIhSen+r5HV205bbXJ50/Dvs9Wki4XQTHHbZLwCelzZOibbb/9T/VecVthCVSOfAt5soIPgaVfXQ4JG1Lg6FXXh7nZlOWd2ymWndAUfl3eYS+4CO+VhSB3+Ehd1fZe2FOmnCNIrBP32anpXqrzzwVAAE3ZnheIIhcfxfeNpPeeVu3CReO2X0WObPyfl0sTaT+nPe2wOY89rY/d/JWjpkqmk87L5QaA6dZ1L0s2eal5oqVoQirLnzpj9X895RG8wBFYHAScNjwLr/i9zmTe9uae54tOy0sNHRKhyXGiOvV0mxnqDSl4hy8CSSnPXRv6Frfjmq5NSdoWDYFR9f8z6hDak5PSYWgMTc3k6XDeSlovFNn/us/vWR27Ufx7x6V0kVNnbLdfBWBo9pwdU0iuvkMH7sUc571Hs0Um2efHFQqpZMRB6jf6xEN0NVtRKtJ4oANgbOwWwFa99/Ll6E+GscZsaSLy1jo4SDOyaBQ2itjSyUQ0KJ8DHYLkKriII548CzuFJKtGQneHi+UvUUXO52zUTqvszj0zMRMTu0YSLtpyInaywSVf8obJn6elvgX4jLY0Wy1laYebrYdrU9G7DQ/vLWVWUm22vlYSRRzsaVn4b2EexKjWGbJE6McujhIofcdiO2nkz8pFAP3aHjOve8yc9XYPfPw6FoS/ckeokOABtjgWOfgOxwN4mbkPAXq0kbF4yA03KrNEGXbVe2QZBIs6GpFFaQmr3AYf5yVv/X8l1j7iYwJZ5kgqUD3dfnLVE1vEv0PXWPES39z3u4aDq1SN11oGyt9WbCQBOLDcsBxAlydarFtvjgr6wO2BXRaNIRpQ/aTak1onBh6QvyvvDZWulvUT8Ro7thXg8jsbS+AkJRgYtMPRI4oC2/6uMLB+P3FBF8oDyrm1JKwfDhKJ0Wf2hyjVFuKvJ7IVpEK/tomBD3PRnWP0OsAms/a9fNn5kwFVbLZjlyQ/vmkrlCorEnL6TsFBMYAgJqAxaE8HCVuhvQDqBZQQt1t7S+FTlLyXnHHQh+jpvSBBTaZQoJYIUkfKRA4TJ+HIxtS95Mg/gp81jFf57I931JJmpBXmRT0pb9lJ59pfkeq0s27B1HpPf43tXlaz0VVytL07aoocLe2xWdoduVp4XuMIGCJA/No8U/c9Xacz4I0LWBD+qq2GI/Q6UBlbKtKk4bcqbhea82u9MiV6lWc153dJzKfuX4BCVlUTgzmZt+UVQO90kUw+GkcVPooG8w3Qkb/JXS72XihGrW/rlKfAwmjtOTiie9bOUq3Q8pd/XNX6rw9/ukn/vqwZOLgq7y/MD/K2+t7ybajOn3j9DFlBoSvc/xSyjm/YFoCDClf/NnY8KY/fBA7B/c09Asn7VINxm1hwcXfrhF0yUKNLef3t2NLqjmKiJsWnCbYPuKQqWRzYHW1jxaG4uc2TQ9zBQ7uPnkHrQnYiFcntdGw6dFEoS0IG/PQnA3EJ3GLNj1eYJCalX2/txGvx2lmu2quSRCe7xH0XH8h4jjVWESSzRRbxxQ12R+rVOw9aBsSWscYSsmaSoYbt7155r3AHAzV6M/TuPyRxAOLen48ehRF95E8M6SQ+jPwMse5mtLVddVlctmD3Qaahl5zMLaQCavEqemTerPVRjyrIOiywmpwaXp/52lu4BJHtByHNwYk5AmFCgNjGcR0gu5Tj+qDFva7wnNNWZSFTeN398HxRLmMhj0t+sefyFW9pkHtotxtvxH39tvszwa+e8MypxhXDptr6IznyxTnb4L+1PYIzfiJNxD8DkDei3DF79lBdeVN6NePaBoBn7Vnlf27/zX/nbVnVBdtOfVmvfqnAHBj61UxZt7h+w5beS87xg/vuXAbLh7itZKiuXm3C/a6kyIUOdBlLTz3TWI63uUTK3TslRU4ayOIg1GfeHHIt+/fWSQsI4sG3syWcMihaGTfo5LyAPlt0VtnnwPJA62QXlCS4/ri+xSpBKgyJS1Z5M2iqhCbij/ypZVsEgB6HiyHbcOcNFnV8FSC6FpRILJPoBAdR4ljpg9jhZ/r8xYrR9X2jl8vXoxU9Dc/va4okbgafjtjslwv0+ibvTeo18zaOQNgR6Gz7GIpbclR8rfsrX7RU1+63EG1tKu3wlNJu3KATQzp9ng/0j1ukkz6eP72frgykujl354uV0oLAW7/PXd5RIvXxTzUC2+rbVZfFwfyb3VnVh1uv1ecJ7XQj/tNx/FU/1H7zEPGH/+2qIfaKeUAOqgkD6vK4/IL0oyN38FwZktT/cvCGwhBsLdCeoCeakwTTsVrxqGZd9AquMOpS61wH382Gsc5LKRaXLIEpAU8BbnnGAcPCzONByzm675Er6RMD+OJoD09t1WG3f7u8b1b3xhef2LOx4B3LA5SQPMQjb/+nCrF7pBdu9qbGnlIPw7MDpQEPLFqJ/wFpUV2xU0giuETU8FqzaFGPtrzQj0Fj/EU5jTHyZtdGXpPfmcaKkWtsYU36SWu61MAchFMdD6mvSf7qgkO7j7+bNBI/U+51oQWeaaBF7vumRYTX54cAMYBn7NT+xJoS0+DrwXFU5JS7GOPl+dPOqfvYQu+yBz9tYuLzaGNPElipholTOM4NH6sSlguqbl4xQuDZ5I1K15VDB6H5pnJpzfzq33vh1+kHSwvYYDIKPS2pOGj6VmvqvlWim1vHrjodYC5W9965fRNAd4OipouEGrXtk7g3ic7RXWXMi1A7wa35iYiaW33WMBd228uObc0uXY0LAvrs/5Qe57Ocgoi1R/wP3kUvRTQP7FhtX1IaXbOpzUh7reTeJ1VdquDqKWu1G3EWH4CZ7u8nU572T6Me4b82e3cINpes808k9858dw/Wm18+qTLR691nw9t3GNhVHscLHIFQEaAG8J3+RqHmVhvh/REb9t0e8YLVpOl+PpXue6xV+TbzukJ15w3LureBOAHIxn91P6+JCFxraXhuo+ailHbjHhpYAXzfCDPcud48VWBPUpnKSQea38mH5qGHxwnaFaOGaPGr8Tm7tjRd7hhvG0lgD7aFtyBn2z4iYfga1uXep19uEPbQykm23BFKwXUge/SE6EJIfAAZhwPCBg/gQ+AmeNu5joF9B/HVXwoPUYRqVR1VYN4sOruRFyAEwkOt62JZ/YGe8LD+8bDpcmIqiHoy08gAoJ4bJVT54Wt7eZ0jED4E6KbZhx09IxcULUahufHKZ/NRqMScLuzyfeaRGiy1otce9d1zVr6Icd/QBuDhihxNsx+8ju7rpe0SVuTz2+EB2JbyLNukLqu23aFyCEKXEy2I1mNf9Ux8yq7bG9gTdADigbW1IGdeunys8Wz4NRCR9tyZ47eTNLKRdrh5qlo7o6u6VaP11bCHuYYLo6YC4b6ZfBGAmYJeKECfQaYJiyxQY40HNHf5iN2eHh+oB0VkbXbsmq8axtP0NJNGXBL9zfVJUb9X006qeEc8KmenalusQxV8E5oKUp6OF83+0rA7P89IRfUnmT05k/vEqVHhtQVBwFxS/kZPxZ86Vcw9HZdvZOylDgqJAAAgAElEQVTcYVI1hlgZrW1wikNMeUUZ8HEtc0sNndvDMEWIEi1RcgwFA/DveHlVeTz/29T13G6roMuf3Zj1TZMDb52xD27WFWUfiAM7kJSD0OVAnKr01XOuvXVPU4Gie2iaEzjWELJ7l+hhR6UbP3tkNDlXK2gY6ugBdxM2+FyrA/IWcG4ve5Mb73Y7d7bI7nv+Jyf2iXNnlIt29J5eLx3jOqvRr4aciF/CVns9uT5381SqeImcEYl4tsPRbKYFb7QfgXwZqPh43IUrF+FwIuIbn+57jdBQhEfa8av1UZsBQ7WQG1+6f580oA793vE+WMkF7qd+9EkoPtPq6ZOzmc3xXch9d+k4fNpZNbH5pbgoXD1QkfCmO3Fol87OtTNgwtb5zq5lyescAUMELLUXij9+XbmERnOxwOnTooc52bh/Vl3rlcbTC2w3aM3RoFLPSHmjrd1DuzwNtsEE2dktwXa68sWeik0nJO2Ozm6VyhQSa/aTHqWzDcu/3P4/H6HHFQZrUz87DpZvA/ewgCvpulaaXn2tZN8hs0BH3Nrh2G9//YE8EOOxH8Sorn+WoroG4qCeD2DVw4cP5bZ//kMOwSclH6NntHB+T7X54oe5UiMOYM5uiZ8Xhk6jj8rlinHqFk9/fVvkuVEzZZ+6Tqp40VzKdeZL79sCjFQyN0Y8uQ1tBmff6cx/10BDM5wBQ7rKt7+YduO3UMlAyEzC69B38TLO7aI0DH/aghjipXPNxXoCLN4XF73hKB0jDPsV0D5cN6zPh+Pyd6FCUpz30X/8oevInIQgbmjD29CB3GDjixoX1byRoqVTUt+3p2VHNr9i+EadA3RxbyEWM/24ct66rh936txK1z0vJ6zCfqfO7/vwzkeLwFv/0qISgPUtFzs0Z8xsnjRpKFqw21Q3yog7m06ONYFexeEVvFoWj7lbMDqNT64zktMF2/Hx6cI2VtTiFo/eJXjRyU6YlvxX8y5KxxxKCb/on8OoRRjC5lYRp8P46qoL4iR7Cmp64cErZ3+X9LTUFiBPGr107p8D5fTLTPww0uXSXMvps8Oe7SnR6MvIeD+yFLh9SHtGTlzYrsqe7ZEh6bylCFGXqy6gG7u1I9kPZl+DPEpLM7IoV8WUGLGGb0K0L0napqOYSZgaciHeR51PjjuZHGssa2YBQb3ih8ROxlueJefl5YmAVpcvz7dckLeKWF2+IG87N6YrRZeTvewI7vWT6+zw7jw91OaGdOBR1LYBikECR1BDxSZfb/bAzCKaIkRdTt9F0McGapiq3rXKvXODt9fdH0u0IWlZJMZkMKymn+2tm4oviwvW5w4vyn9lnA5Okol/LicEuC6f87fJdXlg6FaSLm9COHRueQUGaREplFMGYVLmrGERpVjoqeaiyxdaJs6fI7DACHBdPmeAuS4PDB3V5YHvlwdmFOEU5IKWPcJlJOLFF7smmIvvS0BiLiJHgCPAEeAIRAQCwfixR4SgXAiOAEeAI8AR4AhwBHQR4LpcFxbeyBHgCHAEOAIcgSWDANflS+ar4oJyBDgCHAGOAEdAFwGuy3Vh4Y0cAY4AR4AjwBFYMghwXb5kviouKEeAI8AR4AhwBHQR4LpcFxbeyBHgCHAEOAIcgSWDANflS+arWhGC6uZAw6hqPtHX/aOBQVIxfLrwYJSYz0Mc7p857+UIcAQ4ApGGwAq4Xx5pkHN5/CAQM9td5IITVkuGQoRZyfsaBqfBOBi7QquU+r4Yz8wQY7FNNvT1ZrKR2hQypaSJ9qp06JeMA8rq0/NWjgBHgCOwcAisCF3uPt/o3le8feFQ5JzDhkC05URsTZWDUefT3SQ6ujVdk3s00Iwx29YpQVXlDGZUYUer1woMp1hNLFjdyK9CI7vaYDjwIkcgPAh462vHS2054WHGuSx/BPzpcszdWXBRD4I99arkXXok4WtjU5D5CWuKWUpLzwmzEvHWX+2A7blxJD3JXgzgurt1X/gk4pwWFIGMpzbDxPR3s5BBU6L1/DBdstkSQJGrAqpL0rlqGqQidNeQFGcAKVExEDX93TRkhC/NuTwJL3AEwoKAmJrsjQulQbMTcp35JFILejwnnCMCVw5tyRd/Z56r6b0gpTQl3Jguhfnvmm/WK0nNVXnS1F3KECxhTrbTSReYgapeoeJHl7suX5R0p5Se5GA8zflxZMwN2Yk63BaiKe5gy8BBwhi1dZ3+BFRhY7rVB0LedKzGNpGUoLnFiRjA1ZWQnT2qP5C3RiIC0eaS1Ewxt+ls71drrWVCntPZmck1qIr1njXpb6XeZTKV4Xl5V6xok1fvrTHRmefJTK7I9VDkbRGCgKXW05W6K2swBHESkpIBJBeREMZx0nkgQFKa3jr15cOfnkUmJAVq+snkn47vkDmmlI/89KqxosTh5zf23nwoZDT3/OXX6Vt+rc6LyqZV/V2zzFe/YKzLO5zwqZRHnB0bX9xQYG8bA6LXg37c5+1tFntIQ4Lm7Tqwt0mVN50EYLcciHW6AYxxDJo9J1wkBNR76wblh6yzXSkbpgqdvIdn6n3KRhyF1tuXk3eJ4nbLRfpK+TSLh4C1aup+1cJM5623deTVliQsDPcly/XKIaLI5ZTkI0Pf4KsMe2CHoJvh22GIU/S6z2u6Pzj/ESS2icQA5lf/fKo96dinV95UVgOJb154+CYAUfPVPgy0Dca6PNf+oZZYrCfus9ONskG3XrN7ZAQseh3zbnOfrztHTOhxak7Z5XYnLji2h7LgUHPgtUeAgD+HMkwVWgVZ4n5dK5v3qwlQkqKSZcHdl0QHOvW+XBg47Tz+c+bJoLOUamfjdY7ACkJgfLgfFLPwCnpxf6/q+Utlw3O7e8mOXHh2nLn58IxUCeIzMQl3mn9taT++Q8KWtuiNNMdt0mvWtBnrcg3hfKoddjx3f69sPiyMxrqq7b2wp8zXr20OCw6jOXh7xCMw7WmHzXk/1BR1S6LGbv7KgbpfeqTzcqmOn9Ot617WrgwmWoomGBKhqDOW/7T5oMQblikCTtvrjVDx+2X6dnN9Lbej/VpW3p/lXfUc+OQdf/jTcXace8QNWXlJbFMo5TDocvf5PamoUIVH7RbHes8dzd54lNKo7OHYosnbreYgsjX6GBu7BbA1ab1RP9vOyCn5AcjdfmQQvecIqZABXeGDR/Itxbi4Eh72Zfd/Wg970RdPPRHDCvReU+FMB+Z7XZC7aH4J0mssyc9oS7MVBbeUpQHxVP8+uTYtPSbNQpePvvty9jRd/brB+7Grx/EaRyB0BLz1uZaKG+K44hZPNfk3rHkYmhcrujp0DN2C1xsdllF561KpWr2IbnS0W3cKZjjAGxemauUTqM7yx1CLC09l1mOVRlOIJCvrg1rUcUPNOq9pfN++BXgW2k+uKvqrCE3J2YdnthnC1H4y6Vhim7/zdcOhQsd8Y8WgAku1J7VODDwgf1feGypdXdiIB9XCs/1dob1+P1FOQnnAxdrDUb1lO3ZJXcikFUqzz49LDAJ9ekevA2wyawzsOqMuv7OxBE5SIQcG7XD0iCIk8XX3IwM5faevtoWwxfcV+Aza0+HmqPymRJFDvcD/gevYrb2l8OmV97b03vaKwqCeXr0XJKDIa7JAIRFR5CNlIoeJk3BkY+pecuTPnxAQQDu87V5Ws9FVcrS9O2qKHC3tsUa2+hDm4qQcgXkggJrSUpF2Yeq+h/zdqrhdaM6v9aoZ/q38sbfhPCW476yEyixbp5qA1BJslyiTC8XaPlwHmF8BaYr7F0A7BSHIejv5M0EGnKL/dUaGnGqhveUVwGWESKNdK2jnXCH1b4cHADbCf+f9duA/bj78ify1lXxzOP3kFRaAL/+4qvVfhd6HP31ZM3BwVd5ffH7ScTWwZdUTW1YVQRvrN8fyCa48P11OjOe7Wyfskokbfc7r9988Vd0R3OSowDwjsMWaz5xqJyalX2/t9HnhYBka0DXBdmUNkWixbr3pwNN04QlBhqv2qqQrwlokcd/FB/KLjzVWIQ7vZosc0T3Qnn7u6h30wP8wl7aNNZaQFY8MFGx/98p7wAI13tbau3+7xAGI9/6gHYYlIUXO/MMIAdTiRY6ar5463JxmUtNEFWQdLqOO65NTw+szX6vN9qVRj+A1jsACI0AM1+wm2FzS1vLK12+fc7LzfjycrOyzTaXnK57/uLbew1L4K4/Wvk3WCso+O6f6VgUwUxCCG698dr+K9WT6ejDojZS/yZd737cDXQAN1ajI5XtiO858WZP118oPcDsuPgOQN6JsxJ89VFe+rav6tHA5VqLBvfuhdroa6I2vfGLLr5nhCklwpXnZ2K9ebUJbsaTIhQnR6Sw99UxjOd4HC0ICohH3UTrW/rxFx9YUBDM/JLt3CjpVIImPZ10JgpfhHFjEa29+ptLrcjsd17dY1YDEJafA0auuD3MF/a2pEi6JofsY6k2+lNr6qhx9/uSN1eukDvDrM1FDo/G8pkiPhGlzkLtqTF0uTt6blMu8wBFYSAQ62z6G4pYc1RTW/ZUvWqpr91tsJrH9DZvKYG5O2AA9w7jNUVvRVUyUire9uef5ojilAUuEQ2Wbo8pCfmApwenTjCI3lXZ4SlUDeMUfAlnlb0tua5Ts2f/z2+cOf/m/7jeFe2jb6tvVFnXzb3ZnVR9uvVafp24X5jC/+vfmsVVFfzxjVV1S9yeAum8+unx8eAi2FmjPqhPNSYLxWa261NMyNfGYGc+eJwZwCDE1tzLd/oumhK0AtzzjgGFh5vEEKQOzb1ZPFl98Ys/GgncsUggd4pG3/1N5kw3ukV642Zsae0o9DGCP0oDnEa3vbFxNtZXWpUChWsalNeknreny++E++4u1rwXlaq4ayFxaozoeUmUmwsG5j7+bPCVGkrnXhRZ4poEWue+bFhFenx8CntHbkLFT+wNpSkwDui02GXCPS34RLo94wWpEwI4bH74BX9+wrHubbSRlyRRPCDYcM2m7eT0YBJ7dmIU29jjtV4hDu8ZGDO9CP5u80S/zvH/9Hfy1yfHtoTef9Uun37lavzmo1ju3bwLobnGCGk6IUHOrrfRBjxQI6Q4b3d/m88xXBjr39rJjW7NLV18UBRG85FRSqR3lVF1ShbgXvAs0Vt2O1XZYgRpd8VPLSCv8yvFJ0fc5tfLh97SzDr3bqLVcQszv5+zdIdhcss4vjdI58909WG9++aSKvyKPQgi0kanzIkcgNATc7q8BdoY2Zi7UPAzcXFALeszAuBu2adV5VvycHdHR3o5LhI+G0Eo/F10+n/Py9Ru26Lw2PX5O0L6hDiE2CftX5RRZn8pfK5FB93wdo9OcDeqwef4ykBc5kD16QnQAJC5+4jG5JDk6AUjFID4FbzvXMbDvONARBP3SJ5lp7e+VDdzt3UKKM1NZ5maYGe6epe836+1ZY3kBb505auqmg3rjyZ+nIerJzDVKVHa/w2YmZmJihQBzful4J0dgvggkJj6vw8Lr7ofnU/0YGOlOOsmkM1SniWzi/T6E4Dbu8vkzBwSIOV3YghsNxpBt6NFWqjodJx5z21JEPU0JXj/jYRnQY3i2IZTyfHR53MFDu6/bG66q5iM+XFsLcvzp8rHGs2FTUUQG0PG2c1W3JrAudSoZw1/BJUVTQSxayDdqvNOFqTTeduL8LA5jjQc03vvEbk+PD8IvbaRxxOwp9xSZ8jKltCXkpplsEv+RRGhPO1ybGtPeXXN8fEYZYFDC8++Up+M1MV/v/GwwEG+oRyVncl1uACZvDicC5pLyN3oq/tSp4unpuHwjY+cOk9LYPzqqVAAcf2uEV/LJUXcwjymvKOPr5g4VB4DR2ganOFqfABwN/t3rGA7BiLFcaRKteOit8nTD+2n//eU32377G0H30Utr/17IHqh7/rep67ndVlGXC3HiVPi0/89HAL8r0DtNV9HpV+ajywFy7a17mgqUS2g0Dwoca2BvnZF5UdX1XnIKDpLjZ4+MJoueaGRXfe6Mcj0Mz62lq+qusxrdpi+/IAOc27uH2YVj5HbnTubmt9FQ2h4OGSAuvyB9q73+PTRU3DyVKij1WEYk4tkOR7OZFrzRfgTyZY+8+Hjchasu4xHf+PRdFj/LdL+vtWQ68VS7u89X6Wrln707QZti4qwlUTB0T08lz0btlHX/tLNqYvNLcVF43Ry38kUknTlxaDc6gO/5oU/Opaadmtc5AuFGwFJ7ofhj5gKYpyF/UyWcPq1ydrtRaav1SjN3lhd+9vzp/YyrmtRj8JlgO02useU2yOocr5LbIFfmkGCzFd9Q33NznFj3XwkqGZA5WhFutLUL+0dPg20wQeZgMPOKaCYhV5+7duy38s7b/cEfD0P5n6Wj7h0Hy7eBG0O6Ss+10vTqayX75OQrlOCbAfnbwUCteBO95KzsGC8NBKA6/tYI2t79PasePnwo9//zH7flslRg05Shr5ZOhjTirSbFijE84lWCsajDp4CKPzlmNjVmZ5+6rkRZURGIUvmePbNu8BjSVb79xbSLZ9i+Lf5l8AqZ1iQ8yCfDnzYjAnjpXHVvXr4vLl9UQ0pmapL6RbXawLXL+nw4LiMJCgJ0juXynz90HWFfhZw9N8zIoVtJ9drTsreaQkkiwODFce19M4VAVcIEKt3TJap85+ji3kLsXVHMAbwyxlvX9eNO+WBeadc9LyesIFO856bQ8hJHwBCBt/6lxbePiQMD2jAvo7Un2ndU5V3ZlfW2mDFFN9ILwxQvrNcyd9jEHjZWjN7xOSuDJlaMwluJJ2MQr0YhDX8p4Rf94WcaLo4BQsGQ5Cu41RaebUzwdqlNReCbJ43NrSIOySofaffJ1/LLTOwNqMulSfmnIQJktXH7kPaMnGjuq7Jnu+Hgldah0eXa1ydB14UNuLYH64yPuk6v0EQXB5jvXEcxY9hW1PF9vup8ctzJ5FhjWTNrC+oVPyR2BiMJy4eXVzgCuro8rJiQnb27XH1ZPKwTPCpmEa3LHxUomnmpLl+taeTV0BEgBvaje/dscDFp5dAOsbdp/6f20Lmt7BF4It6cFiIEWi17uNno2BsP4LOfPN6n5j/bexkyxbSq6h7cxaNZXrypId18w9XGV0/Jp/jaAbzOEVgcBOheGUO2CZFevPX70ETv5NbvxQE/Imfh+/IwfS3KIYLAUHOUEKZZlj6bAPvypf+C/A04Ar4ILMC+nB6x49024QlkgZfolt4n35cH/s74vjwwRsFTxBe7JoqDJ+eUHAGOAEdgHghg2Nf7JfMYz4cuMwTm58e+zMDgr8MR4AhwBDgCHIEliADX5UvwS+MicwQ4AhwBjgBHgEGA63IGDF7kCHAEOAIcAY7AEkSA6/Il+KVxkTkCHAGOAEeAI8AgwHU5AwYvcgQ4AhwBjgBHYAkiwHX5EvzSuMgcAY4AR4AjwBFgEOC6nAGDFyMFAQz/EnQ+NCOZMexrMClYtMNnZ+SMbT5dva3BpWjTDtSpe3tCYIXh5z5vFfLF6bAK2IRBZ4XUcwEpOQFHgCOwRBHgcd+W6Be3vMXGIGvZcNxVUxRbGGwMdh9AYuJefqkfs6rIwd59KHQbZrttLk1kOJaucyIMkdjF4PB5Gla4ghn71UmdmPMYfi6rzlFzPFUnUj0rnHG5r8oBJ6xSAjpjOt7DEeAILE0E5qvL3ecb3fuKty/Nl+dSRwYCs72ts+kF0Wph1qS/lTr8PkSpW0mtZ9qbEW3ybfdtyYjPSZno/GrakqFh7kuqatFX/0J+lzJ9VlLuFhUfowryP1yGLzLeOxmdrmRlxRXMU84iR5c6K4zAxPRCLNxh+enkj5G7UZiuWFVqGVAyycpUvBDRCHjra8dLbTkRLSMXLoIQCEaXYwrR0nOCzCRP2vqrHbA9N07K+rW7dV8EvQ8XZQkiMHu3obumQV/wT4oG9Tr09+veun4oY/e1a9JfSoUYVvvi3rcP3tLNvCLMs+bJFLirN6X/NlMZVc9qIproZa2haSEjLl1NDxCdWRL1ScOYtyAtimaQU/pTUl97CxxFDukEABGwZrb2906mMasBgRxzscMk9PVm+nlNhTEvRRwCYnKzNy6UBi2akMpMLxNa0Cw4YSQicOXQlnz5t1E3SZokdSBdTtN0YibTB0JKcqzGNpF8nbnFibn2B66E7Gw5/6rEkn9yBEJBYKbV04eKSs4sTra/3yfrJzrDvSwmUoPC5qcAM0Fm+E4z0VI0oWrNS82546iR8puJXe+Px8vTqajFClqkDczssXrkBm09/Z80wOa8ey1FXQZ523QGRhWYN0+ACXuUtC7CWw9+8n7qa81WlaGiIM1nNQAw+fO0by449VQ6G3c1Aa89SgQstZ6u1F1ZuqtYA7kSkpLxn4lBJ29emgiQjKi3Tn358Kdnqfzfnsn7bVIe6OQ8pd3+dbnrwN4mVUpy1N8TlgOxTjdmqF+a8HCpIwuByXFHw0xMyWZZRXkvD06iZf3yeHxZnNwo2dWnnV+sfa2Ztusocvpm2kNobGS3v8Q0DS8xnPXg8GNj1yPXayNrjnuowuO7u/ryzOkxdN6gTqyjLWh+B+bcARc3ZPliNdF5Zlr7xzJ99+KKDDPd30/CzKTN0am0Yam7hqRvZx/X56C2w7OdvLzEELBWTd2vWhiZvfW2jrzakoSF4c65GiDg/uD8R7gRf1NQ5Ej07KG68qb06tPtr9bn6Yzxp8vd5+vOAZrQ49TjssvtzrYx2B6vbuY1jsAcEEAPtWbmH1hPfwtROTOT7TOftGv2JcSubjnJGsznMN+iDJEUOVq/Z8QJMd2qFXfDaFQA1ggh9vpY/numIFOAZdppu5fFOABGFcTDccfn24zU8OzYNUzfbmUN73wXvijf+rKdZHy4H/SUx7J94ch4sZGhb6ArcYTdNpt/szurumnkW8iTFbwiqx9d7qq298KeMl+/tsR99oMKhwAlX+c435YALHj3SkFg2lk1gXti81eqY29URS3tqMjjo/CsWPEUWyBQZu8OwXxs7JK0WSYfAcUzddT0RQ4A9sgfvd7M6PVWIxkVvN9BPBoeBG87RpFTltQr0Gawq56cGl5vflmF0uyP6DQXyuGAj+C8YQUj4LS93ggVv1/BCDyiV09KeQ7gry3tx3cEt5Ayvl8+NnYLYGvS+mBexH1+z+rYjfRvz9kxaQQ5XN+YamcO1DUttCoMPNBBRil8ChvRjE/c6whb+1W2K5aZQpqKfy59BHBv2i3cmzKVPeUp6vfSV0L3MarI0aNtTZRKRS3gGxM/82afv9rUzSXxJn/ToiGdOJAf1mpf9ZiMtMO1qTGAR/viO9LuaMuJWGj/wYuVyXEPrMPzBXrcgGQO1P2qv/chOQ8mJ3RunHu7warnaR/9zBq1ELz26BHw1uea1z0m/pXj8k7nYWhyG5ifUoUUvd4kJrvqPUq7UEI3OqnXrDsFM9y8zsaey3SWU9le+RjgRmWWKKfOFNopeT08CCS+eeHhTzdV5nTP/zZ1PbfbqrMpxymN9+Xe0esA+82M/dNAwsvvbLyVdOXBBKFEZZx6pDG/pZicpvs6x2layOm7HWD8bOGO2wBX39lYRfkQJvZRciQv0RtOgbPwZ1kg4K1D93KrRdTWaJEGZ1GXJw/67jwtHpAH+ZrtvgfD2pGbtQ1snRjD2bpSjomzFNAa2S4PgvrmmOSybrUoA0hpZkKysrPt5GThcWeR58dJMMkLFLxBVzJrwiHd956kBnbTC5mFZUa371gnAJn1bFSmrysAWhqinpRnkWn13kLu5IWFRgA15euNb1yY6sghM3ka8jeZ808722wmZuK/lT82nHzLM2XGNlTqlixbwlQtpWeIEmyXpmxYR4a1TLM4pCLtwtR9YQjRzeopCM+KG698dv8S/VdLqvm1sgw51fc91cjGcWLdqcSuDn5erkb3EdSunK2GU18eIv8cdB5jXa5DrNvUBNsHXLliV6LFutXuaBsrPhjqafpVOypyFz2bT9x38cE+dq4wTcGy5OUIQgB35K67L8mKXJAM74bNdOLZeZ5Z8YALQuYYtYr1GUF90DSt5Hh7QtMWuNrAWLl7+h2w+XCz7t43Kmen7hk/Lho07WvSC5DDdPe1tZnCogF+aCnqpo54BKLOIWqWp9LqeOdR3YynEPqP1hVOomLfQmrjnwuOADFcoyKXFbO5pK3Fva7wnNNWpSwGP0ZFfqlU/OU2lZ6vuLyptv5IjtQSQMjR2reJIpengJzqWxX5m5QpCAFR5MyMAF8PjgP8/+1db2gURxQfQShqChEimhLM5i4kmugHL2o05ZqWQ6JBiJCiLcXQ1Kbxw6Xa2haRQPPhDLVHsNajpkY9sR9a0oYqpilpCCEIIanNBWr8F3PmtNdEIdCjyPWLJX0zu3u3f2b3br1cPOvbD7mdmTdv3v4U3r6Z93srJFCNw08Dgb7WavLlXDwVTmtD6r58d43syKnugoJ12iWSancQl0R744jPzxIcxdiVCQjMRJc3qXK12DkxDXwPti6lJDTIPOfki+lNT+ZgmBd2w6Z3V6mkDjziSVIlM9ZotD20Ks6X068p9jhK98h59TAF/Poe6pXpJexXH5wnjIYDD2crCuKvL8VryiTN2ZW+UoHpBDTkTrHN/mqyCOUR+RGWjbYTF2/7XRbE34VDYLDnLKm7qI6wqxo8W1xtvgZXLDTf51a5bbttLQlMwH6lQVymNj/U1xXYVJuv6qQaPD29R11054kJeL3xVwciNA4EG1UTsJEpCPS1Lup+de5EhYk9xr5csG0lZDx4j0BZmPRfDdud6V8EV8hIBHKV28g0bqZ08xiRmjlaSChTFI1RZo0pn4huJhc1cYNjpZjp/cyjmdvh8+3LDhq6vWgoQASH2Soz/itGpW9IMTv0D0cjcPzPMSQ69sOjVB9BpRbS2iO5FUD5W1qWB4VllGXmVHLYWEAEgndvEkdNoWZFobDUPCzOL9pCLt0JEYmdqJmuad6bGCZXh10rPtb0kzqpgwqsPSxoh7GdecPIh94AAAW6SURBVAiAI/+84E6fmSMHo419OYuwIf0NL0RgIRCQtojBT2uPnGF1Yb+z8v41UpE16A8TmjUW5kTqtEZKll1/MGzF+tCvYSh3qnLkt28pXiMkXQl28s12Edgm/+YcniMnke5rg2TV2/IjRKYfkdUruZLJPlNgiu7Mw/YGIdllWROJiuQkqxblUkJgcvIqITUpqUhqMpaBSwqmzBYKfvsydeRval/9dFYb+3Kyeu1G0tE9COXWNVomz7T0uFosn4jr1sYORIDVcQP3zFjXXWWjtaPawm1KjFbb45vhyn52T2ukbLMLun4rHbT6qZZKy3PMkcCsQWBtZTWFLGOysbYiPwDy5nLzzDYAFAq4txDlh+G1QxAHc/OrKkZMtxy4SrBz3hEoLNxEJnRaQ5PXiXZXXCVkKZKmQTxkFBtflqJ8YzU4klYEhho3TDX/fUTjgrlLGnPSSP77B3aT3z5rG9BMvNLWbau2mtoW08HS42MtvHm+EYiOTa+U2F/SETWcCuvIYJQe5qwsJus3a5LFlODNjvqJQZaZUszsXqomu/nhF+2zZnIQ4zr4gbX5LJNRSj1nz6hw3vT4PwGXjH6dxVAri/LXVMkn9yCXvXN95f3RVD6fargYDlhAwF5/aF+g+figakpw4NKwo2aHEO+8fldFQuv95QJ5o5oedSdzCdtqHVe7BlQaCLnr8/dLs/kCpNevJ7Yp11NoUHbjfRoQgKKtZ0rGjuxQqR5qPDCk6pAbJr6cksq63yIde5V8bvjOSn+NSDmTVZj90o36zqNn7kkyUxecJ+5sJZ0780pEQrnZXBz7/yMAmdsm7tnC81M3TEulWpiiE2VvA0352XBCD+68duG/+U3L1MQvqPpCeDlucQnTO1oNPmuXnMQni0JRmrIcOM5/ki+7yzrwN3UEXL5zdWffqfaFJFWUk+YhXq8q2W3Y444JAOVs13ebvA2KVLUEVtjcXg/xlCtY6UAld5PXYhpsbncdEMeVnHKgn122qWyARWAXYbinL8iWC/rdt2wxDQkswOGUEJj86tMbn5zTMNCgsOt4sVV+OTNj+7Ebj7e3LHaWfChZtbs73CJVgoNCLns7oXtnXmfDNzdOQTa73EOcJTfFHuI8deWw07ljcQubDx9lOV5V377/8TGW6RaTh8G9JR1URKEfWrKA6RJ0Gl7PNQJQ1J1mm6f0WhBqp5VqpLcBmnBXMHak9zw41+JVlrHlHbErlfAJ7oGH8EGX9VJRl+jYyQdFTeXmh+WR6QcT0ysYk02pHs7dWfq9WLheNQKNHJdvzaybJgFwiG1aYWynCwFgb/cDn1vOTXN4xoNqJ1rkGf9+289QB0b6YkrdxWBPskG5aDTNSy9028uXeMS27vic2lAENiyRH1JJk5P7iL3e5+0pX2dvhp4tzSMiIT42ijdpQuCPn378/fTIxtM69e918X35orm5uZjwv3+aHrDE5PAGEXhSBD4a+cB4KktiNx428D3AvZ5a3loqGE9MOALOb/SlcpfMK1PKx0+ylb3ivSZLjnXKHDB9zRZxDst9435khWX/5dAheCLNh1llCj61UI2S9jifjs4mINmrlWg1iHbi3/lEoOmVi/OpjqOLRvaTh9RkcY7Ys9dle+H6s2f0Alv8YhksuHiBF8XlEAFTBOC8nPvVbebeOFvoktsTTJWaD4K3hsKrMYK4RpjzYXLqdI2/yqqZr29y2eEgJhLEQXmtXnl0+etVG6RXDUqR129z0ncIf4SVeeeM6qygSuztI3/VcNHWiWNHpiHAPlheJFd6CX39LmzR9+v/W2Sa2WhP2hDAuDxt0KJiHgKmcTlvAvYhAs8+AmmIy9kRO3DbxAt24Nus7cDLMzP9F+PyxP9CGJcnxgglEAFEABHIRASg7Os/9ZloGNr0dBAwzWN/OibhqogAIoAIIAKIACJgAQHVHruFeSiKCCACiAAigAggApmBwH/X9ed4eeSlhgAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E6%95%B0%E6%8D%AE%E8%A7%A3%E9%87%8A.png](attachment:%E6%95%B0%E6%8D%AE%E8%A7%A3%E9%87%8A.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:00.678905Z",
     "start_time": "2021-06-04T17:47:00.614901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No  No phone service             DSL             No          Yes               No          No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes                No             DSL            Yes           No              Yes          No   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes                No             DSL            Yes          Yes               No          No   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No  No phone service             DSL            Yes           No              Yes         Yes   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes                No     Fiber optic             No           No               No          No   \n",
       "\n",
       "  StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges Churn  \n",
       "0          No              No  Month-to-month              Yes           Electronic check           29.85        29.85    No  \n",
       "1          No              No        One year               No               Mailed check           56.95       1889.5    No  \n",
       "2          No              No  Month-to-month              Yes               Mailed check           53.85       108.15   Yes  \n",
       "3          No              No        One year               No  Bank transfer (automatic)           42.30      1840.75    No  \n",
       "4          No              No  Month-to-month              Yes           Electronic check           70.70       151.65   Yes  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'D:/LKM/电信客户流失识别/data/train.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:03.384059Z",
     "start_time": "2021-06-04T17:47:03.191048Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>missing</th>\n",
       "      <th>unique</th>\n",
       "      <th>mean_or_top1</th>\n",
       "      <th>std_or_top2</th>\n",
       "      <th>min_or_top3</th>\n",
       "      <th>1%_or_top4</th>\n",
       "      <th>10%_or_top5</th>\n",
       "      <th>50%_or_bottom5</th>\n",
       "      <th>75%_or_bottom4</th>\n",
       "      <th>90%_or_bottom3</th>\n",
       "      <th>99%_or_bottom2</th>\n",
       "      <th>max_or_bottom1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>7043</td>\n",
       "      <td>4102-HLENU:0.01%</td>\n",
       "      <td>5622-UEJFI:0.01%</td>\n",
       "      <td>3871-IKPYH:0.01%</td>\n",
       "      <td>3144-KMTWZ:0.01%</td>\n",
       "      <td>0784-GTUUK:0.01%</td>\n",
       "      <td>3566-HJGPK:0.01%</td>\n",
       "      <td>2085-JVGAD:0.01%</td>\n",
       "      <td>4706-AXVKM:0.01%</td>\n",
       "      <td>3312-UUMZW:0.01%</td>\n",
       "      <td>8992-OBVDG:0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2</td>\n",
       "      <td>Male:50.48%</td>\n",
       "      <td>Female:49.52%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Male:50.48%</td>\n",
       "      <td>Female:49.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>int64</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2</td>\n",
       "      <td>0.162147</td>\n",
       "      <td>0.368612</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2</td>\n",
       "      <td>No:51.70%</td>\n",
       "      <td>Yes:48.30%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:51.70%</td>\n",
       "      <td>Yes:48.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2</td>\n",
       "      <td>No:70.04%</td>\n",
       "      <td>Yes:29.96%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:70.04%</td>\n",
       "      <td>Yes:29.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>int64</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>73</td>\n",
       "      <td>32.3711</td>\n",
       "      <td>24.5595</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>55</td>\n",
       "      <td>69</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes:90.32%</td>\n",
       "      <td>No:9.68%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Yes:90.32%</td>\n",
       "      <td>No:9.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>No:48.13%</td>\n",
       "      <td>Yes:42.18%</td>\n",
       "      <td>No phone service:9.68%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:48.13%</td>\n",
       "      <td>Yes:42.18%</td>\n",
       "      <td>No phone service:9.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>Fiber optic:43.96%</td>\n",
       "      <td>DSL:34.37%</td>\n",
       "      <td>No:21.67%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Fiber optic:43.96%</td>\n",
       "      <td>DSL:34.37%</td>\n",
       "      <td>No:21.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>No:49.67%</td>\n",
       "      <td>Yes:28.67%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:49.67%</td>\n",
       "      <td>Yes:28.67%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>No:43.84%</td>\n",
       "      <td>Yes:34.49%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:43.84%</td>\n",
       "      <td>Yes:34.49%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>No:43.94%</td>\n",
       "      <td>Yes:34.39%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:43.94%</td>\n",
       "      <td>Yes:34.39%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>No:49.31%</td>\n",
       "      <td>Yes:29.02%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:49.31%</td>\n",
       "      <td>Yes:29.02%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>No:39.90%</td>\n",
       "      <td>Yes:38.44%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:39.90%</td>\n",
       "      <td>Yes:38.44%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>No:39.54%</td>\n",
       "      <td>Yes:38.79%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:39.54%</td>\n",
       "      <td>Yes:38.79%</td>\n",
       "      <td>No internet service:21.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>Month-to-month:55.02%</td>\n",
       "      <td>Two year:24.07%</td>\n",
       "      <td>One year:20.91%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Month-to-month:55.02%</td>\n",
       "      <td>Two year:24.07%</td>\n",
       "      <td>One year:20.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes:59.22%</td>\n",
       "      <td>No:40.78%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Yes:59.22%</td>\n",
       "      <td>No:40.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>4</td>\n",
       "      <td>Electronic check:33.58%</td>\n",
       "      <td>Mailed check:22.89%</td>\n",
       "      <td>Bank transfer (automatic):21.92%</td>\n",
       "      <td>Credit card (automatic):21.61%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Electronic check:33.58%</td>\n",
       "      <td>Mailed check:22.89%</td>\n",
       "      <td>Bank transfer (automatic):21.92%</td>\n",
       "      <td>Credit card (automatic):21.61%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>float64</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>1585</td>\n",
       "      <td>64.7617</td>\n",
       "      <td>30.09</td>\n",
       "      <td>18.25</td>\n",
       "      <td>19.2</td>\n",
       "      <td>20.05</td>\n",
       "      <td>70.35</td>\n",
       "      <td>89.85</td>\n",
       "      <td>102.6</td>\n",
       "      <td>114.729</td>\n",
       "      <td>118.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>6531</td>\n",
       "      <td>:0.16%</td>\n",
       "      <td>20.2:0.16%</td>\n",
       "      <td>19.75:0.13%</td>\n",
       "      <td>20.05:0.11%</td>\n",
       "      <td>19.9:0.11%</td>\n",
       "      <td>2110.15:0.01%</td>\n",
       "      <td>2462.6:0.01%</td>\n",
       "      <td>6590.8:0.01%</td>\n",
       "      <td>1341.5:0.01%</td>\n",
       "      <td>587.45:0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>object</td>\n",
       "      <td>7043</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2</td>\n",
       "      <td>No:73.46%</td>\n",
       "      <td>Yes:26.54%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No:73.46%</td>\n",
       "      <td>Yes:26.54%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     type  size missing  unique             mean_or_top1          std_or_top2                       min_or_top3                      1%_or_top4       10%_or_top5  \\\n",
       "customerID         object  7043   0.00%    7043         4102-HLENU:0.01%     5622-UEJFI:0.01%                  3871-IKPYH:0.01%                3144-KMTWZ:0.01%  0784-GTUUK:0.01%   \n",
       "gender             object  7043   0.00%       2              Male:50.48%        Female:49.52%                              None                            None              None   \n",
       "SeniorCitizen       int64  7043   0.00%       2                 0.162147             0.368612                                 0                               0                 0   \n",
       "Partner            object  7043   0.00%       2                No:51.70%           Yes:48.30%                              None                            None              None   \n",
       "Dependents         object  7043   0.00%       2                No:70.04%           Yes:29.96%                              None                            None              None   \n",
       "tenure              int64  7043   0.00%      73                  32.3711              24.5595                                 0                               1                 2   \n",
       "PhoneService       object  7043   0.00%       2               Yes:90.32%             No:9.68%                              None                            None              None   \n",
       "MultipleLines      object  7043   0.00%       3                No:48.13%           Yes:42.18%            No phone service:9.68%                            None              None   \n",
       "InternetService    object  7043   0.00%       3       Fiber optic:43.96%           DSL:34.37%                         No:21.67%                            None              None   \n",
       "OnlineSecurity     object  7043   0.00%       3                No:49.67%           Yes:28.67%        No internet service:21.67%                            None              None   \n",
       "OnlineBackup       object  7043   0.00%       3                No:43.84%           Yes:34.49%        No internet service:21.67%                            None              None   \n",
       "DeviceProtection   object  7043   0.00%       3                No:43.94%           Yes:34.39%        No internet service:21.67%                            None              None   \n",
       "TechSupport        object  7043   0.00%       3                No:49.31%           Yes:29.02%        No internet service:21.67%                            None              None   \n",
       "StreamingTV        object  7043   0.00%       3                No:39.90%           Yes:38.44%        No internet service:21.67%                            None              None   \n",
       "StreamingMovies    object  7043   0.00%       3                No:39.54%           Yes:38.79%        No internet service:21.67%                            None              None   \n",
       "Contract           object  7043   0.00%       3    Month-to-month:55.02%      Two year:24.07%                   One year:20.91%                            None              None   \n",
       "PaperlessBilling   object  7043   0.00%       2               Yes:59.22%            No:40.78%                              None                            None              None   \n",
       "PaymentMethod      object  7043   0.00%       4  Electronic check:33.58%  Mailed check:22.89%  Bank transfer (automatic):21.92%  Credit card (automatic):21.61%              None   \n",
       "MonthlyCharges    float64  7043   0.00%    1585                  64.7617                30.09                             18.25                            19.2             20.05   \n",
       "TotalCharges       object  7043   0.00%    6531                   :0.16%           20.2:0.16%                       19.75:0.13%                     20.05:0.11%        19.9:0.11%   \n",
       "Churn              object  7043   0.00%       2                No:73.46%           Yes:26.54%                              None                            None              None   \n",
       "\n",
       "                    50%_or_bottom5           75%_or_bottom4         90%_or_bottom3                    99%_or_bottom2                  max_or_bottom1  \n",
       "customerID        3566-HJGPK:0.01%         2085-JVGAD:0.01%       4706-AXVKM:0.01%                  3312-UUMZW:0.01%                8992-OBVDG:0.01%  \n",
       "gender                        None                     None                   None                       Male:50.48%                   Female:49.52%  \n",
       "SeniorCitizen                    0                        0                      1                                 1                               1  \n",
       "Partner                       None                     None                   None                         No:51.70%                      Yes:48.30%  \n",
       "Dependents                    None                     None                   None                         No:70.04%                      Yes:29.96%  \n",
       "tenure                          29                       55                     69                                72                              72  \n",
       "PhoneService                  None                     None                   None                        Yes:90.32%                        No:9.68%  \n",
       "MultipleLines                 None                     None              No:48.13%                        Yes:42.18%          No phone service:9.68%  \n",
       "InternetService               None                     None     Fiber optic:43.96%                        DSL:34.37%                       No:21.67%  \n",
       "OnlineSecurity                None                     None              No:49.67%                        Yes:28.67%      No internet service:21.67%  \n",
       "OnlineBackup                  None                     None              No:43.84%                        Yes:34.49%      No internet service:21.67%  \n",
       "DeviceProtection              None                     None              No:43.94%                        Yes:34.39%      No internet service:21.67%  \n",
       "TechSupport                   None                     None              No:49.31%                        Yes:29.02%      No internet service:21.67%  \n",
       "StreamingTV                   None                     None              No:39.90%                        Yes:38.44%      No internet service:21.67%  \n",
       "StreamingMovies               None                     None              No:39.54%                        Yes:38.79%      No internet service:21.67%  \n",
       "Contract                      None                     None  Month-to-month:55.02%                   Two year:24.07%                 One year:20.91%  \n",
       "PaperlessBilling              None                     None                   None                        Yes:59.22%                       No:40.78%  \n",
       "PaymentMethod                 None  Electronic check:33.58%    Mailed check:22.89%  Bank transfer (automatic):21.92%  Credit card (automatic):21.61%  \n",
       "MonthlyCharges               70.35                    89.85                  102.6                           114.729                          118.75  \n",
       "TotalCharges         2110.15:0.01%             2462.6:0.01%           6590.8:0.01%                      1341.5:0.01%                    587.45:0.01%  \n",
       "Churn                         None                     None                   None                         No:73.46%                      Yes:26.54%  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detect(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T13:47:50.405116Z",
     "start_time": "2021-06-04T13:47:50.399116Z"
    }
   },
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取建模数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:06.347229Z",
     "start_time": "2021-06-04T17:47:06.341229Z"
    }
   },
   "outputs": [],
   "source": [
    "feature = data.iloc[:, 1:-1]\n",
    "label = data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:08.200335Z",
     "start_time": "2021-06-04T17:47:08.190334Z"
    }
   },
   "outputs": [],
   "source": [
    "feature['TotalCharges'] = feature['TotalCharges'].apply(lambda x: 0 if x == ' ' else float(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 连续特征分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:10.506467Z",
     "start_time": "2021-06-04T17:47:10.397461Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = ['tenure', 'MonthlyCharges', 'TotalCharges']\n",
    "for col in cols:\n",
    "    feature[col] = LabelEncoder().fit_transform(pd.cut(feature[col], bins=6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:13.163619Z",
     "start_time": "2021-06-04T17:47:13.063613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <th>tenure_0</th>\n",
       "      <th>tenure_1</th>\n",
       "      <th>tenure_2</th>\n",
       "      <th>tenure_3</th>\n",
       "      <th>tenure_4</th>\n",
       "      <th>tenure_5</th>\n",
       "      <th>MonthlyCharges_0</th>\n",
       "      <th>MonthlyCharges_1</th>\n",
       "      <th>MonthlyCharges_2</th>\n",
       "      <th>MonthlyCharges_3</th>\n",
       "      <th>MonthlyCharges_4</th>\n",
       "      <th>MonthlyCharges_5</th>\n",
       "      <th>TotalCharges_0</th>\n",
       "      <th>TotalCharges_1</th>\n",
       "      <th>TotalCharges_2</th>\n",
       "      <th>TotalCharges_3</th>\n",
       "      <th>TotalCharges_4</th>\n",
       "      <th>TotalCharges_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen Partner Dependents PhoneService     MultipleLines OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies PaperlessBilling  \\\n",
       "0  Female              0     Yes         No           No  No phone service             No          Yes               No          No          No              No              Yes   \n",
       "1    Male              0      No         No          Yes                No            Yes           No              Yes          No          No              No               No   \n",
       "2    Male              0      No         No          Yes                No            Yes          Yes               No          No          No              No              Yes   \n",
       "3    Male              0      No         No           No  No phone service            Yes           No              Yes         Yes          No              No               No   \n",
       "4  Female              0      No         No          Yes                No             No           No               No          No          No              No              Yes   \n",
       "\n",
       "   Contract_Month-to-month  Contract_One year  Contract_Two year  InternetService_DSL  InternetService_Fiber optic  InternetService_No  PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                      1.0                0.0                0.0                  1.0                          0.0                 0.0                                      0.0   \n",
       "1                      0.0                1.0                0.0                  1.0                          0.0                 0.0                                      0.0   \n",
       "2                      1.0                0.0                0.0                  1.0                          0.0                 0.0                                      0.0   \n",
       "3                      0.0                1.0                0.0                  1.0                          0.0                 0.0                                      1.0   \n",
       "4                      1.0                0.0                0.0                  0.0                          1.0                 0.0                                      0.0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  PaymentMethod_Mailed check  tenure_0  tenure_1  tenure_2  tenure_3  tenure_4  tenure_5  \\\n",
       "0                                    0.0                             1.0                         0.0       1.0       0.0       0.0       0.0       0.0       0.0   \n",
       "1                                    0.0                             0.0                         1.0       0.0       0.0       1.0       0.0       0.0       0.0   \n",
       "2                                    0.0                             0.0                         1.0       1.0       0.0       0.0       0.0       0.0       0.0   \n",
       "3                                    0.0                             0.0                         0.0       0.0       0.0       0.0       1.0       0.0       0.0   \n",
       "4                                    0.0                             1.0                         0.0       1.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "   MonthlyCharges_0  MonthlyCharges_1  MonthlyCharges_2  MonthlyCharges_3  MonthlyCharges_4  MonthlyCharges_5  TotalCharges_0  TotalCharges_1  TotalCharges_2  TotalCharges_3  \\\n",
       "0               1.0               0.0               0.0               0.0               0.0               0.0             1.0             0.0             0.0             0.0   \n",
       "1               0.0               0.0               1.0               0.0               0.0               0.0             0.0             1.0             0.0             0.0   \n",
       "2               0.0               0.0               1.0               0.0               0.0               0.0             1.0             0.0             0.0             0.0   \n",
       "3               0.0               1.0               0.0               0.0               0.0               0.0             0.0             1.0             0.0             0.0   \n",
       "4               0.0               0.0               0.0               1.0               0.0               0.0             1.0             0.0             0.0             0.0   \n",
       "\n",
       "   TotalCharges_4  TotalCharges_5  \n",
       "0             0.0             0.0  \n",
       "1             0.0             0.0  \n",
       "2             0.0             0.0  \n",
       "3             0.0             0.0  \n",
       "4             0.0             0.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def onehot_model(train):\n",
    "    one_hot = OneHotEncoder(handle_unknown='ignore').fit(train)  # 定义独热编码模型\n",
    "    feature_names = one_hot.get_feature_names(train.columns)  # 将独热编码模型训练后的特征名取出\n",
    "    return one_hot, feature_names\n",
    "\n",
    "def pred_onehot(model, feature_names, pred_dataframe):\n",
    "    return pd.DataFrame(model.transform(pred_dataframe).toarray(),columns=feature_names)\n",
    "\n",
    "columns = ['Contract', 'InternetService', 'PaymentMethod', 'tenure', 'MonthlyCharges', 'TotalCharges']\n",
    "one_hot, feature_names = onehot_model(feature[columns])\n",
    "feature[feature_names] = pred_onehot(one_hot, feature_names, feature[columns])\n",
    "\n",
    "for col in columns:\n",
    "    del feature[col]\n",
    "    \n",
    "feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:16:07.308898Z",
     "start_time": "2021-06-04T17:16:07.279896Z"
    }
   },
   "source": [
    "## 单变量处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:22.205136Z",
     "start_time": "2021-06-04T17:47:22.197135Z"
    }
   },
   "outputs": [],
   "source": [
    "feature['gender'] = feature['gender'].apply(lambda x: 1 if x == 'Male' else 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:24.709279Z",
     "start_time": "2021-06-04T17:47:24.614274Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <th>tenure_0</th>\n",
       "      <th>tenure_1</th>\n",
       "      <th>tenure_2</th>\n",
       "      <th>tenure_3</th>\n",
       "      <th>tenure_4</th>\n",
       "      <th>tenure_5</th>\n",
       "      <th>MonthlyCharges_0</th>\n",
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       "      <th>MonthlyCharges_2</th>\n",
       "      <th>MonthlyCharges_3</th>\n",
       "      <th>MonthlyCharges_4</th>\n",
       "      <th>MonthlyCharges_5</th>\n",
       "      <th>TotalCharges_0</th>\n",
       "      <th>TotalCharges_1</th>\n",
       "      <th>TotalCharges_2</th>\n",
       "      <th>TotalCharges_3</th>\n",
       "      <th>TotalCharges_4</th>\n",
       "      <th>TotalCharges_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  PhoneService  MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  TechSupport  StreamingTV  StreamingMovies  \\\n",
       "0       2              0        1           0             0              0               0             1                 0            0            0                0   \n",
       "1       1              0        0           0             1              0               1             0                 1            0            0                0   \n",
       "2       1              0        0           0             1              0               1             1                 0            0            0                0   \n",
       "3       1              0        0           0             0              0               1             0                 1            1            0                0   \n",
       "4       2              0        0           0             1              0               0             0                 0            0            0                0   \n",
       "\n",
       "   PaperlessBilling  Contract_Month-to-month  Contract_One year  Contract_Two year  InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "0                 1                      1.0                0.0                0.0                  1.0                          0.0                 0.0   \n",
       "1                 0                      0.0                1.0                0.0                  1.0                          0.0                 0.0   \n",
       "2                 1                      1.0                0.0                0.0                  1.0                          0.0                 0.0   \n",
       "3                 0                      0.0                1.0                0.0                  1.0                          0.0                 0.0   \n",
       "4                 1                      1.0                0.0                0.0                  0.0                          1.0                 0.0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  PaymentMethod_Mailed check  tenure_0  tenure_1  tenure_2  \\\n",
       "0                                      0.0                                    0.0                             1.0                         0.0       1.0       0.0       0.0   \n",
       "1                                      0.0                                    0.0                             0.0                         1.0       0.0       0.0       1.0   \n",
       "2                                      0.0                                    0.0                             0.0                         1.0       1.0       0.0       0.0   \n",
       "3                                      1.0                                    0.0                             0.0                         0.0       0.0       0.0       0.0   \n",
       "4                                      0.0                                    0.0                             1.0                         0.0       1.0       0.0       0.0   \n",
       "\n",
       "   tenure_3  tenure_4  tenure_5  MonthlyCharges_0  MonthlyCharges_1  MonthlyCharges_2  MonthlyCharges_3  MonthlyCharges_4  MonthlyCharges_5  TotalCharges_0  TotalCharges_1  \\\n",
       "0       0.0       0.0       0.0               1.0               0.0               0.0               0.0               0.0               0.0             1.0             0.0   \n",
       "1       0.0       0.0       0.0               0.0               0.0               1.0               0.0               0.0               0.0             0.0             1.0   \n",
       "2       0.0       0.0       0.0               0.0               0.0               1.0               0.0               0.0               0.0             1.0             0.0   \n",
       "3       1.0       0.0       0.0               0.0               1.0               0.0               0.0               0.0               0.0             0.0             1.0   \n",
       "4       0.0       0.0       0.0               0.0               0.0               0.0               1.0               0.0               0.0             1.0             0.0   \n",
       "\n",
       "   TotalCharges_2  TotalCharges_3  TotalCharges_4  TotalCharges_5  \n",
       "0             0.0             0.0             0.0             0.0  \n",
       "1             0.0             0.0             0.0             0.0  \n",
       "2             0.0             0.0             0.0             0.0  \n",
       "3             0.0             0.0             0.0             0.0  \n",
       "4             0.0             0.0             0.0             0.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup', \n",
    "        'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'PaperlessBilling']\n",
    "for col in cols:\n",
    "    feature[col] = feature[col].apply(lambda x: 1 if x == 'Yes' else 0)\n",
    "    \n",
    "feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标签转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:27.356431Z",
     "start_time": "2021-06-04T17:47:27.349430Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "label = label.apply(lambda x: 1 if x == 'Yes' else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:30.162591Z",
     "start_time": "2021-06-04T17:47:30.149590Z"
    }
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(feature, label, \n",
    "                                                    test_size=0.4, \n",
    "                                                    random_state=0)\n",
    "\n",
    "x_test, x_valid, y_test, y_valid = train_test_split(x_test, y_test, \n",
    "                                                    test_size=0.5, \n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练集标签分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:32.739738Z",
     "start_time": "2021-06-04T17:47:32.733738Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3103\n",
       "1    1122\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不均衡样本处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:35.154877Z",
     "start_time": "2021-06-04T17:47:35.004868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3103\n",
       "0    3103\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_smote, y_smote = SMOTE().fit_resample(x_train, y_train)\n",
    "y_smote.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:37.431007Z",
     "start_time": "2021-06-04T17:47:37.421006Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    plt.figure(figsize=(12,6))\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    " \n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    " \n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分类结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:39.556128Z",
     "start_time": "2021-06-04T17:47:39.549128Z"
    }
   },
   "outputs": [],
   "source": [
    "def class_report(model, x_val, y_val):\n",
    "    pred = model.predict(x_val)\n",
    "    print('AUC：', roc_auc_score(y_val, pred))\n",
    "    print('分类报告：\\n', classification_report(y_val, pred))\n",
    "    print('混淆矩阵：\\n', confusion_matrix(y_val, pred))\n",
    "    \n",
    "    pred = model.predict_proba(x_val)\n",
    "    fpr, tpr, threshold = roc_curve(y_val, pred[:, 1])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, label='ROC Area:{}'.format(round(roc_auc,2)))\n",
    "    plt.plot([0,1], [0,1], 'k--')\n",
    "    plt.xlabel('False position rate')\n",
    "    plt.ylabel('True position rate')\n",
    "    plt.title('ROC Curve')\n",
    "    plt.legend(loc='best')\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lightgbm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:47:42.473295Z",
     "start_time": "2021-06-04T17:47:42.117275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[1]\ttraining's binary_logloss: 0.670529\ttraining's auc: 0.87128\tvalid_1's binary_logloss: 0.677201\tvalid_1's auc: 0.806649\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[2]\ttraining's binary_logloss: 0.649417\ttraining's auc: 0.888712\tvalid_1's binary_logloss: 0.662945\tvalid_1's auc: 0.819086\n",
      "[3]\ttraining's binary_logloss: 0.630367\ttraining's auc: 0.892396\tvalid_1's binary_logloss: 0.650403\tvalid_1's auc: 0.817006\n",
      "[4]\ttraining's binary_logloss: 0.612764\ttraining's auc: 0.896928\tvalid_1's binary_logloss: 0.639272\tvalid_1's auc: 0.818496\n",
      "[5]\ttraining's binary_logloss: 0.597374\ttraining's auc: 0.898942\tvalid_1's binary_logloss: 0.630073\tvalid_1's auc: 0.817376\n",
      "[6]\ttraining's binary_logloss: 0.58311\ttraining's auc: 0.901865\tvalid_1's binary_logloss: 0.621032\tvalid_1's auc: 0.819721\n",
      "[7]\ttraining's binary_logloss: 0.569798\ttraining's auc: 0.902506\tvalid_1's binary_logloss: 0.613074\tvalid_1's auc: 0.820735\n",
      "[8]\ttraining's binary_logloss: 0.557289\ttraining's auc: 0.903123\tvalid_1's binary_logloss: 0.605272\tvalid_1's auc: 0.821433\n",
      "[9]\ttraining's binary_logloss: 0.546226\ttraining's auc: 0.903723\tvalid_1's binary_logloss: 0.599506\tvalid_1's auc: 0.821621\n",
      "[10]\ttraining's binary_logloss: 0.535141\ttraining's auc: 0.905349\tvalid_1's binary_logloss: 0.593772\tvalid_1's auc: 0.822247\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[10]\ttraining's binary_logloss: 0.535141\ttraining's auc: 0.905349\tvalid_1's binary_logloss: 0.593772\tvalid_1's auc: 0.822247\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(bagging_fraction=0.8, colsample_bytree=0.8, feature_fraction=0.8,\n",
       "               learning_rate=0.05, max_depth=50, n_estimators=10, nthread=-1,\n",
       "               num_leaves=50, objective='binary', random_state=0,\n",
       "               scale_pos_weight=2, subsample_freq=1)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb = LGBMClassifier(boosting_type='gbdt', \n",
    "                     objective='binary', \n",
    "                     num_leaves=50,\n",
    "                     learning_rate=0.05, \n",
    "                     n_estimators=10,\n",
    "                     max_depth=50,\n",
    "                     colsample_bytree=0.8,\n",
    "                     subsample_freq=1,\n",
    "                     reg_alpha=0.0,\n",
    "                     reg_lambda=0.0,\n",
    "                     bagging_fraction=0.8, \n",
    "                     feature_fraction=0.8, \n",
    "                     nthread=-1,\n",
    "                     scale_pos_weight=2,\n",
    "                     random_state=0)\n",
    "lgb.fit(x_smote, y_smote, \n",
    "        early_stopping_rounds=100, \n",
    "        eval_metric=[\"logloss\", \"auc\"], \n",
    "        eval_set=[(x_smote, y_smote), (x_valid, y_valid)], \n",
    "        verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:50:10.336753Z",
     "start_time": "2021-06-04T17:49:40.879068Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=-1 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=0.8 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (lightgbm)\"\n",
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
    "estimate = LGBMClassifier(bagging_fraction=0.8, colsample_bytree=0.8, feature_fraction=0.8,\n",
    "                          learning_rate=0.05, max_depth=50, n_estimators=10, nthread=-1,\n",
    "                          num_leaves=50, objective='binary', random_state=0,\n",
    "                          scale_pos_weight=2, subsample_freq=1)\n",
    "plot_learning_curve(estimate, title, x_smote, y_smote, ylim=(0.7, 1.01), cv=cv, n_jobs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分类评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:50:55.004307Z",
     "start_time": "2021-06-04T17:50:54.652287Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC： 0.7310918259767643\n",
      "分类报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.65      0.75      1038\n",
      "           1       0.45      0.82      0.58       371\n",
      "\n",
      "    accuracy                           0.69      1409\n",
      "   macro avg       0.68      0.73      0.67      1409\n",
      "weighted avg       0.79      0.69      0.71      1409\n",
      "\n",
      "混淆矩阵：\n",
      " [[670 368]\n",
      " [ 68 303]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_report(lgb, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:41:01.503361Z",
     "start_time": "2021-06-04T17:40:54.559964Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.57937\tvalidation_0-auc:0.88030\tvalidation_1-logloss:0.59972\tvalidation_1-auc:0.81000\n",
      "Multiple eval metrics have been passed: 'validation_1-auc' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-auc hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-logloss:0.51602\tvalidation_0-auc:0.88907\tvalidation_1-logloss:0.55406\tvalidation_1-auc:0.81202\n",
      "[2]\tvalidation_0-logloss:0.47487\tvalidation_0-auc:0.89418\tvalidation_1-logloss:0.52522\tvalidation_1-auc:0.81951\n",
      "[3]\tvalidation_0-logloss:0.44643\tvalidation_0-auc:0.89905\tvalidation_1-logloss:0.51004\tvalidation_1-auc:0.81881\n",
      "[4]\tvalidation_0-logloss:0.42485\tvalidation_0-auc:0.90246\tvalidation_1-logloss:0.50076\tvalidation_1-auc:0.81942\n",
      "[5]\tvalidation_0-logloss:0.40921\tvalidation_0-auc:0.90610\tvalidation_1-logloss:0.49334\tvalidation_1-auc:0.82239\n",
      "[6]\tvalidation_0-logloss:0.39712\tvalidation_0-auc:0.90914\tvalidation_1-logloss:0.49117\tvalidation_1-auc:0.82172\n",
      "[7]\tvalidation_0-logloss:0.38743\tvalidation_0-auc:0.91149\tvalidation_1-logloss:0.49186\tvalidation_1-auc:0.82035\n",
      "[8]\tvalidation_0-logloss:0.37864\tvalidation_0-auc:0.91475\tvalidation_1-logloss:0.48817\tvalidation_1-auc:0.82112\n",
      "[9]\tvalidation_0-logloss:0.37161\tvalidation_0-auc:0.91695\tvalidation_1-logloss:0.48800\tvalidation_1-auc:0.82051\n",
      "[10]\tvalidation_0-logloss:0.36609\tvalidation_0-auc:0.91873\tvalidation_1-logloss:0.48901\tvalidation_1-auc:0.82037\n",
      "[11]\tvalidation_0-logloss:0.36200\tvalidation_0-auc:0.91987\tvalidation_1-logloss:0.49028\tvalidation_1-auc:0.81942\n",
      "[12]\tvalidation_0-logloss:0.35900\tvalidation_0-auc:0.92049\tvalidation_1-logloss:0.49081\tvalidation_1-auc:0.81954\n",
      "[13]\tvalidation_0-logloss:0.35590\tvalidation_0-auc:0.92174\tvalidation_1-logloss:0.49115\tvalidation_1-auc:0.81938\n",
      "[14]\tvalidation_0-logloss:0.35403\tvalidation_0-auc:0.92213\tvalidation_1-logloss:0.49231\tvalidation_1-auc:0.81901\n",
      "[15]\tvalidation_0-logloss:0.34919\tvalidation_0-auc:0.92433\tvalidation_1-logloss:0.49157\tvalidation_1-auc:0.81950\n",
      "[16]\tvalidation_0-logloss:0.34446\tvalidation_0-auc:0.92666\tvalidation_1-logloss:0.49098\tvalidation_1-auc:0.82001\n",
      "[17]\tvalidation_0-logloss:0.34042\tvalidation_0-auc:0.92802\tvalidation_1-logloss:0.49098\tvalidation_1-auc:0.81999\n",
      "[18]\tvalidation_0-logloss:0.33613\tvalidation_0-auc:0.92983\tvalidation_1-logloss:0.49262\tvalidation_1-auc:0.81899\n",
      "[19]\tvalidation_0-logloss:0.33422\tvalidation_0-auc:0.93047\tvalidation_1-logloss:0.49260\tvalidation_1-auc:0.81960\n",
      "[20]\tvalidation_0-logloss:0.33005\tvalidation_0-auc:0.93217\tvalidation_1-logloss:0.49342\tvalidation_1-auc:0.81906\n",
      "[21]\tvalidation_0-logloss:0.32480\tvalidation_0-auc:0.93473\tvalidation_1-logloss:0.49687\tvalidation_1-auc:0.81756\n",
      "[22]\tvalidation_0-logloss:0.32188\tvalidation_0-auc:0.93565\tvalidation_1-logloss:0.49801\tvalidation_1-auc:0.81706\n",
      "[23]\tvalidation_0-logloss:0.31816\tvalidation_0-auc:0.93721\tvalidation_1-logloss:0.49964\tvalidation_1-auc:0.81627\n",
      "[24]\tvalidation_0-logloss:0.31349\tvalidation_0-auc:0.93927\tvalidation_1-logloss:0.50107\tvalidation_1-auc:0.81565\n",
      "[25]\tvalidation_0-logloss:0.30925\tvalidation_0-auc:0.94117\tvalidation_1-logloss:0.50032\tvalidation_1-auc:0.81649\n",
      "[26]\tvalidation_0-logloss:0.30714\tvalidation_0-auc:0.94190\tvalidation_1-logloss:0.50137\tvalidation_1-auc:0.81610\n",
      "[27]\tvalidation_0-logloss:0.30529\tvalidation_0-auc:0.94259\tvalidation_1-logloss:0.50213\tvalidation_1-auc:0.81595\n",
      "[28]\tvalidation_0-logloss:0.30353\tvalidation_0-auc:0.94319\tvalidation_1-logloss:0.50223\tvalidation_1-auc:0.81635\n",
      "[29]\tvalidation_0-logloss:0.30239\tvalidation_0-auc:0.94345\tvalidation_1-logloss:0.50214\tvalidation_1-auc:0.81698\n",
      "[30]\tvalidation_0-logloss:0.29924\tvalidation_0-auc:0.94507\tvalidation_1-logloss:0.50479\tvalidation_1-auc:0.81487\n",
      "[31]\tvalidation_0-logloss:0.29720\tvalidation_0-auc:0.94565\tvalidation_1-logloss:0.50642\tvalidation_1-auc:0.81446\n",
      "[32]\tvalidation_0-logloss:0.29441\tvalidation_0-auc:0.94674\tvalidation_1-logloss:0.50863\tvalidation_1-auc:0.81354\n",
      "[33]\tvalidation_0-logloss:0.29224\tvalidation_0-auc:0.94774\tvalidation_1-logloss:0.51065\tvalidation_1-auc:0.81267\n",
      "[34]\tvalidation_0-logloss:0.29052\tvalidation_0-auc:0.94838\tvalidation_1-logloss:0.51153\tvalidation_1-auc:0.81210\n",
      "[35]\tvalidation_0-logloss:0.28867\tvalidation_0-auc:0.94895\tvalidation_1-logloss:0.51231\tvalidation_1-auc:0.81193\n",
      "[36]\tvalidation_0-logloss:0.28637\tvalidation_0-auc:0.94982\tvalidation_1-logloss:0.51317\tvalidation_1-auc:0.81184\n",
      "[37]\tvalidation_0-logloss:0.28509\tvalidation_0-auc:0.95017\tvalidation_1-logloss:0.51403\tvalidation_1-auc:0.81137\n",
      "[38]\tvalidation_0-logloss:0.28255\tvalidation_0-auc:0.95106\tvalidation_1-logloss:0.51676\tvalidation_1-auc:0.81043\n",
      "[39]\tvalidation_0-logloss:0.28016\tvalidation_0-auc:0.95195\tvalidation_1-logloss:0.52022\tvalidation_1-auc:0.80917\n",
      "[40]\tvalidation_0-logloss:0.27816\tvalidation_0-auc:0.95277\tvalidation_1-logloss:0.52160\tvalidation_1-auc:0.80829\n",
      "[41]\tvalidation_0-logloss:0.27594\tvalidation_0-auc:0.95359\tvalidation_1-logloss:0.52391\tvalidation_1-auc:0.80714\n",
      "[42]\tvalidation_0-logloss:0.27322\tvalidation_0-auc:0.95465\tvalidation_1-logloss:0.52641\tvalidation_1-auc:0.80642\n",
      "[43]\tvalidation_0-logloss:0.27162\tvalidation_0-auc:0.95519\tvalidation_1-logloss:0.52917\tvalidation_1-auc:0.80507\n",
      "[44]\tvalidation_0-logloss:0.27042\tvalidation_0-auc:0.95553\tvalidation_1-logloss:0.53098\tvalidation_1-auc:0.80435\n",
      "[45]\tvalidation_0-logloss:0.26927\tvalidation_0-auc:0.95581\tvalidation_1-logloss:0.53203\tvalidation_1-auc:0.80422\n",
      "[46]\tvalidation_0-logloss:0.26819\tvalidation_0-auc:0.95618\tvalidation_1-logloss:0.53312\tvalidation_1-auc:0.80366\n",
      "[47]\tvalidation_0-logloss:0.26531\tvalidation_0-auc:0.95740\tvalidation_1-logloss:0.53421\tvalidation_1-auc:0.80372\n",
      "[48]\tvalidation_0-logloss:0.26343\tvalidation_0-auc:0.95816\tvalidation_1-logloss:0.53486\tvalidation_1-auc:0.80364\n",
      "[49]\tvalidation_0-logloss:0.26195\tvalidation_0-auc:0.95864\tvalidation_1-logloss:0.53672\tvalidation_1-auc:0.80298\n",
      "[50]\tvalidation_0-logloss:0.26092\tvalidation_0-auc:0.95892\tvalidation_1-logloss:0.53586\tvalidation_1-auc:0.80382\n",
      "[51]\tvalidation_0-logloss:0.25972\tvalidation_0-auc:0.95934\tvalidation_1-logloss:0.53763\tvalidation_1-auc:0.80339\n",
      "[52]\tvalidation_0-logloss:0.25740\tvalidation_0-auc:0.96013\tvalidation_1-logloss:0.53936\tvalidation_1-auc:0.80251\n",
      "[53]\tvalidation_0-logloss:0.25598\tvalidation_0-auc:0.96046\tvalidation_1-logloss:0.53974\tvalidation_1-auc:0.80306\n",
      "[54]\tvalidation_0-logloss:0.25429\tvalidation_0-auc:0.96095\tvalidation_1-logloss:0.54258\tvalidation_1-auc:0.80233\n",
      "[55]\tvalidation_0-logloss:0.25173\tvalidation_0-auc:0.96196\tvalidation_1-logloss:0.54346\tvalidation_1-auc:0.80189\n",
      "[56]\tvalidation_0-logloss:0.25038\tvalidation_0-auc:0.96229\tvalidation_1-logloss:0.54435\tvalidation_1-auc:0.80195\n",
      "[57]\tvalidation_0-logloss:0.24884\tvalidation_0-auc:0.96282\tvalidation_1-logloss:0.54608\tvalidation_1-auc:0.80112\n",
      "[58]\tvalidation_0-logloss:0.24763\tvalidation_0-auc:0.96318\tvalidation_1-logloss:0.54761\tvalidation_1-auc:0.80080\n",
      "[59]\tvalidation_0-logloss:0.24667\tvalidation_0-auc:0.96346\tvalidation_1-logloss:0.54922\tvalidation_1-auc:0.80026\n",
      "[60]\tvalidation_0-logloss:0.24554\tvalidation_0-auc:0.96383\tvalidation_1-logloss:0.54932\tvalidation_1-auc:0.80045\n",
      "[61]\tvalidation_0-logloss:0.24502\tvalidation_0-auc:0.96401\tvalidation_1-logloss:0.55007\tvalidation_1-auc:0.80006\n",
      "[62]\tvalidation_0-logloss:0.24344\tvalidation_0-auc:0.96456\tvalidation_1-logloss:0.55097\tvalidation_1-auc:0.80012\n",
      "[63]\tvalidation_0-logloss:0.24236\tvalidation_0-auc:0.96498\tvalidation_1-logloss:0.55305\tvalidation_1-auc:0.79879\n",
      "[64]\tvalidation_0-logloss:0.24177\tvalidation_0-auc:0.96513\tvalidation_1-logloss:0.55428\tvalidation_1-auc:0.79876\n",
      "[65]\tvalidation_0-logloss:0.24045\tvalidation_0-auc:0.96556\tvalidation_1-logloss:0.55523\tvalidation_1-auc:0.79910\n",
      "[66]\tvalidation_0-logloss:0.24008\tvalidation_0-auc:0.96571\tvalidation_1-logloss:0.55514\tvalidation_1-auc:0.79925\n",
      "[67]\tvalidation_0-logloss:0.23894\tvalidation_0-auc:0.96609\tvalidation_1-logloss:0.55619\tvalidation_1-auc:0.79907\n",
      "[68]\tvalidation_0-logloss:0.23802\tvalidation_0-auc:0.96637\tvalidation_1-logloss:0.55797\tvalidation_1-auc:0.79832\n",
      "[69]\tvalidation_0-logloss:0.23634\tvalidation_0-auc:0.96691\tvalidation_1-logloss:0.55959\tvalidation_1-auc:0.79787\n",
      "[70]\tvalidation_0-logloss:0.23540\tvalidation_0-auc:0.96716\tvalidation_1-logloss:0.56156\tvalidation_1-auc:0.79749\n",
      "[71]\tvalidation_0-logloss:0.23495\tvalidation_0-auc:0.96724\tvalidation_1-logloss:0.56341\tvalidation_1-auc:0.79685\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[72]\tvalidation_0-logloss:0.23250\tvalidation_0-auc:0.96818\tvalidation_1-logloss:0.56577\tvalidation_1-auc:0.79633\n",
      "[73]\tvalidation_0-logloss:0.23175\tvalidation_0-auc:0.96842\tvalidation_1-logloss:0.56756\tvalidation_1-auc:0.79556\n",
      "[74]\tvalidation_0-logloss:0.23060\tvalidation_0-auc:0.96871\tvalidation_1-logloss:0.57001\tvalidation_1-auc:0.79512\n",
      "[75]\tvalidation_0-logloss:0.22946\tvalidation_0-auc:0.96907\tvalidation_1-logloss:0.57056\tvalidation_1-auc:0.79529\n",
      "[76]\tvalidation_0-logloss:0.22837\tvalidation_0-auc:0.96944\tvalidation_1-logloss:0.57248\tvalidation_1-auc:0.79477\n",
      "[77]\tvalidation_0-logloss:0.22716\tvalidation_0-auc:0.96979\tvalidation_1-logloss:0.57366\tvalidation_1-auc:0.79494\n",
      "[78]\tvalidation_0-logloss:0.22587\tvalidation_0-auc:0.97020\tvalidation_1-logloss:0.57536\tvalidation_1-auc:0.79422\n",
      "[79]\tvalidation_0-logloss:0.22503\tvalidation_0-auc:0.97047\tvalidation_1-logloss:0.57646\tvalidation_1-auc:0.79433\n",
      "[80]\tvalidation_0-logloss:0.22428\tvalidation_0-auc:0.97062\tvalidation_1-logloss:0.57663\tvalidation_1-auc:0.79454\n",
      "[81]\tvalidation_0-logloss:0.22368\tvalidation_0-auc:0.97071\tvalidation_1-logloss:0.57636\tvalidation_1-auc:0.79500\n",
      "[82]\tvalidation_0-logloss:0.22206\tvalidation_0-auc:0.97132\tvalidation_1-logloss:0.57706\tvalidation_1-auc:0.79461\n",
      "[83]\tvalidation_0-logloss:0.22139\tvalidation_0-auc:0.97147\tvalidation_1-logloss:0.57751\tvalidation_1-auc:0.79485\n",
      "[84]\tvalidation_0-logloss:0.22098\tvalidation_0-auc:0.97159\tvalidation_1-logloss:0.57890\tvalidation_1-auc:0.79446\n",
      "[85]\tvalidation_0-logloss:0.21994\tvalidation_0-auc:0.97192\tvalidation_1-logloss:0.58112\tvalidation_1-auc:0.79416\n",
      "[86]\tvalidation_0-logloss:0.21929\tvalidation_0-auc:0.97211\tvalidation_1-logloss:0.58194\tvalidation_1-auc:0.79378\n",
      "[87]\tvalidation_0-logloss:0.21837\tvalidation_0-auc:0.97238\tvalidation_1-logloss:0.58200\tvalidation_1-auc:0.79395\n",
      "[88]\tvalidation_0-logloss:0.21726\tvalidation_0-auc:0.97265\tvalidation_1-logloss:0.58555\tvalidation_1-auc:0.79278\n",
      "[89]\tvalidation_0-logloss:0.21698\tvalidation_0-auc:0.97269\tvalidation_1-logloss:0.58564\tvalidation_1-auc:0.79304\n",
      "[90]\tvalidation_0-logloss:0.21631\tvalidation_0-auc:0.97287\tvalidation_1-logloss:0.58593\tvalidation_1-auc:0.79348\n",
      "[91]\tvalidation_0-logloss:0.21603\tvalidation_0-auc:0.97303\tvalidation_1-logloss:0.58630\tvalidation_1-auc:0.79360\n",
      "[92]\tvalidation_0-logloss:0.21497\tvalidation_0-auc:0.97333\tvalidation_1-logloss:0.58682\tvalidation_1-auc:0.79342\n",
      "[93]\tvalidation_0-logloss:0.21387\tvalidation_0-auc:0.97364\tvalidation_1-logloss:0.58783\tvalidation_1-auc:0.79357\n",
      "[94]\tvalidation_0-logloss:0.21338\tvalidation_0-auc:0.97371\tvalidation_1-logloss:0.58838\tvalidation_1-auc:0.79350\n",
      "[95]\tvalidation_0-logloss:0.21271\tvalidation_0-auc:0.97388\tvalidation_1-logloss:0.58989\tvalidation_1-auc:0.79336\n",
      "[96]\tvalidation_0-logloss:0.21210\tvalidation_0-auc:0.97401\tvalidation_1-logloss:0.59053\tvalidation_1-auc:0.79342\n",
      "[97]\tvalidation_0-logloss:0.21070\tvalidation_0-auc:0.97433\tvalidation_1-logloss:0.59190\tvalidation_1-auc:0.79329\n",
      "[98]\tvalidation_0-logloss:0.21025\tvalidation_0-auc:0.97445\tvalidation_1-logloss:0.59223\tvalidation_1-auc:0.79327\n",
      "[99]\tvalidation_0-logloss:0.20911\tvalidation_0-auc:0.97474\tvalidation_1-logloss:0.59372\tvalidation_1-auc:0.79280\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier()\n",
    "xgb.fit(x_smote, y_smote, \n",
    "        early_stopping_rounds=100, \n",
    "        eval_metric=[\"logloss\", \"auc\"], \n",
    "        eval_set=[(x_smote, y_smote), (x_valid, y_valid)], \n",
    "        verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:23:08.585994Z",
     "start_time": "2021-06-04T17:22:29.640766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (xgboost)\"\n",
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
    "plot_learning_curve(xgb, title, x_smote, y_smote, ylim=(0.7, 1.01), cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分类评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:41:08.176743Z",
     "start_time": "2021-06-04T17:41:07.856724Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC： 0.7461477338235982\n",
      "分类报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.75      0.81      1038\n",
      "           1       0.51      0.75      0.61       371\n",
      "\n",
      "    accuracy                           0.75      1409\n",
      "   macro avg       0.70      0.75      0.71      1409\n",
      "weighted avg       0.79      0.75      0.76      1409\n",
      "\n",
      "混淆矩阵：\n",
      " [[774 264]\n",
      " [ 94 277]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_report(xgb, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:23:32.235346Z",
     "start_time": "2021-06-04T17:23:31.528306Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_smote, y_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T15:30:16.716665Z",
     "start_time": "2021-06-04T15:29:30.916045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (RandomForest)\"\n",
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
    "plot_learning_curve(rf, title, x_smote, y_smote, ylim=(0.7, 1.01), cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分类评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T15:30:17.032683Z",
     "start_time": "2021-06-04T15:30:16.718665Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC： 0.7083560548224087\n",
      "分类报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.85      0.85      1038\n",
      "           1       0.58      0.57      0.57       371\n",
      "\n",
      "    accuracy                           0.78      1409\n",
      "   macro avg       0.71      0.71      0.71      1409\n",
      "weighted avg       0.77      0.78      0.78      1409\n",
      "\n",
      "混淆矩阵：\n",
      " [[883 155]\n",
      " [161 210]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_report(rf, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ExtraTrees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T15:32:10.492172Z",
     "start_time": "2021-06-04T15:32:09.686126Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "et = ExtraTreesClassifier()\n",
    "et.fit(x_smote, y_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T15:33:33.384914Z",
     "start_time": "2021-06-04T15:32:51.845538Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (ExtraTrees)\"\n",
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
    "plot_learning_curve(et, title, x_smote, y_smote, ylim=(0.7, 1.01), cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分类报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T15:33:44.032523Z",
     "start_time": "2021-06-04T15:33:43.704504Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC： 0.6714862190922829\n",
      "分类报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.85      0.84      1038\n",
      "           1       0.54      0.49      0.52       371\n",
      "\n",
      "    accuracy                           0.76      1409\n",
      "   macro avg       0.68      0.67      0.68      1409\n",
      "weighted avg       0.75      0.76      0.75      1409\n",
      "\n",
      "混淆矩阵：\n",
      " [[882 156]\n",
      " [188 183]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_report(et, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DecisionTree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:41:32.759149Z",
     "start_time": "2021-06-04T17:41:32.727147Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(x_smote, y_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:41:42.152686Z",
     "start_time": "2021-06-04T17:41:35.495305Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (DecisionTree)\"\n",
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
    "plot_learning_curve(dtc, title, x_smote, y_smote, ylim=(0.7, 1.01), cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分类报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T17:42:05.175003Z",
     "start_time": "2021-06-04T17:42:04.950990Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC： 0.6526897049582183\n",
      "分类报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.79      0.80      1038\n",
      "           1       0.46      0.52      0.49       371\n",
      "\n",
      "    accuracy                           0.72      1409\n",
      "   macro avg       0.64      0.65      0.65      1409\n",
      "weighted avg       0.73      0.72      0.72      1409\n",
      "\n",
      "混淆矩阵：\n",
      " [[815 223]\n",
      " [178 193]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\Anaconda3\\\\envs\\\\lkm\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_report(dtc, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:lkm]",
   "language": "python",
   "name": "lkm"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "256px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
